{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Base imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from __future__ import print_function, division\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set()\n",
    "\n",
    "import torch\n",
    "from torch import nn, optim\n",
    "from torch.autograd import Variable\n",
    "from torch.optim import Optimizer\n",
    "\n",
    "\n",
    "import collections\n",
    "import h5py, sys\n",
    "import gzip\n",
    "import os\n",
    "import math\n",
    "\n",
    "\n",
    "try:\n",
    "    import cPickle as pickle\n",
    "except:\n",
    "    import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some utility functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mkdir(paths):\n",
    "    if not isinstance(paths, (list, tuple)):\n",
    "        paths = [paths]\n",
    "    for path in paths:\n",
    "        if not os.path.isdir(path):\n",
    "            os.makedirs(path)\n",
    "\n",
    "from __future__ import print_function\n",
    "import torch\n",
    "from torch import nn, optim\n",
    "from torch.autograd import Variable\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "\n",
    "suffixes = ['B', 'KB', 'MB', 'GB', 'TB', 'PB']\n",
    "def humansize(nbytes):\n",
    "    i = 0\n",
    "    while nbytes >= 1024 and i < len(suffixes)-1:\n",
    "        nbytes /= 1024.\n",
    "        i += 1\n",
    "    f = ('%.2f' % nbytes)\n",
    "    return '%s%s' % (f, suffixes[i])\n",
    "\n",
    "\n",
    "def get_num_batches(nb_samples, batch_size, roundup=True):\n",
    "    if roundup:\n",
    "        return ((nb_samples + (-nb_samples % batch_size)) / batch_size)  # roundup division\n",
    "    else:\n",
    "        return nb_samples / batch_size\n",
    "\n",
    "def generate_ind_batch(nb_samples, batch_size, random=True, roundup=True):\n",
    "    if random:\n",
    "        ind = np.random.permutation(nb_samples)\n",
    "    else:\n",
    "        ind = range(int(nb_samples))\n",
    "    for i in range(int(get_num_batches(nb_samples, batch_size, roundup))):\n",
    "        yield ind[i * batch_size: (i + 1) * batch_size]\n",
    "\n",
    "def to_variable(var=(), cuda=True, volatile=False):\n",
    "    out = []\n",
    "    for v in var:\n",
    "        if isinstance(v, np.ndarray):\n",
    "            v = torch.from_numpy(v).type(torch.FloatTensor)\n",
    "\n",
    "        if not v.is_cuda and cuda:\n",
    "            v = v.cuda()\n",
    "\n",
    "        if not isinstance(v, Variable):\n",
    "            v = Variable(v, volatile=volatile)\n",
    "\n",
    "        out.append(v)\n",
    "    return out\n",
    "  \n",
    "def cprint(color, text, **kwargs):\n",
    "    if color[0] == '*':\n",
    "        pre_code = '1;'\n",
    "        color = color[1:]\n",
    "    else:\n",
    "        pre_code = ''\n",
    "    code = {\n",
    "        'a': '30',\n",
    "        'r': '31',\n",
    "        'g': '32',\n",
    "        'y': '33',\n",
    "        'b': '34',\n",
    "        'p': '35',\n",
    "        'c': '36',\n",
    "        'w': '37'\n",
    "    }\n",
    "    print(\"\\x1b[%s%sm%s\\x1b[0m\" % (pre_code, code[color], text), **kwargs)\n",
    "    sys.stdout.flush()\n",
    "\n",
    "def shuffle_in_unison_scary(a, b):\n",
    "    rng_state = np.random.get_state()\n",
    "    np.random.shuffle(a)\n",
    "    np.random.set_state(rng_state)\n",
    "    np.random.shuffle(b)\n",
    "    \n",
    "    \n",
    "import torch.utils.data as data\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "import h5py\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataloader functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Datafeed(data.Dataset):\n",
    "\n",
    "    def __init__(self, x_train, y_train, transform=None):\n",
    "        self.x_train = x_train\n",
    "        self.y_train = y_train\n",
    "        self.transform = transform\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        img = self.x_train[index]\n",
    "        if self.transform is not None:\n",
    "            img = self.transform(img)\n",
    "        return img, self.y_train[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.x_train)\n",
    "\n",
    "class DatafeedImage(data.Dataset):\n",
    "    def __init__(self, x_train, y_train, transform=None):\n",
    "        self.x_train = x_train\n",
    "        self.y_train = y_train\n",
    "        self.transform = transform\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        img = self.x_train[index]\n",
    "        img = Image.fromarray(np.uint8(img))\n",
    "        if self.transform is not None:\n",
    "            img = self.transform(img)\n",
    "        return img, self.y_train[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Base network wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn.functional as F\n",
    "class BaseNet(object):\n",
    "    def __init__(self):\n",
    "        cprint('c', '\\nNet:')\n",
    "\n",
    "    def get_nb_parameters(self):\n",
    "        return np.sum(p.numel() for p in self.model.parameters())\n",
    "\n",
    "    def set_mode_train(self, train=True):\n",
    "        if train:\n",
    "            self.model.train()\n",
    "        else:\n",
    "            self.model.eval()\n",
    "\n",
    "    def update_lr(self, epoch, gamma=0.99):\n",
    "        self.epoch += 1\n",
    "        if self.schedule is not None:\n",
    "            if len(self.schedule) == 0 or epoch in self.schedule:\n",
    "                self.lr *= gamma\n",
    "                print('learning rate: %f  (%d)\\n' % self.lr, epoch)\n",
    "                for param_group in self.optimizer.param_groups:\n",
    "                    param_group['lr'] = self.lr\n",
    "\n",
    "    def save(self, filename):\n",
    "        cprint('c', 'Writting %s\\n' % filename)\n",
    "        torch.save({\n",
    "            'epoch': self.epoch,\n",
    "            'lr': self.lr,\n",
    "            'model': self.model,\n",
    "            'optimizer': self.optimizer}, filename)\n",
    "\n",
    "    def load(self, filename):\n",
    "        cprint('c', 'Reading %s\\n' % filename)\n",
    "        state_dict = torch.load(filename)\n",
    "        self.epoch = state_dict['epoch']\n",
    "        self.lr = state_dict['lr']\n",
    "        self.model = state_dict['model']\n",
    "        self.optimizer = state_dict['optimizer']\n",
    "        print('  restoring epoch: %d, lr: %f' % (self.epoch, self.lr))\n",
    "        return self.epoch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Our special classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Priors classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class laplace_prior(object):\n",
    "    def __init__(self, mu, b):\n",
    "        self.mu = mu\n",
    "        self.b = b\n",
    "\n",
    "    def loglike(self, x, do_sum=True):\n",
    "        if do_sum:\n",
    "            return (-np.log(2*self.b) - torch.abs(x - self.mu)/self.b).sum()\n",
    "        else:\n",
    "            return (-np.log(2*self.b) - torch.abs(x - self.mu)/self.b)\n",
    "\n",
    "class isotropic_gauss_prior(object):\n",
    "    def __init__(self, mu, sigma):\n",
    "        self.mu = mu\n",
    "        self.sigma = sigma\n",
    "        \n",
    "        self.cte_term = -(0.5)*np.log(2*np.pi)\n",
    "        self.det_sig_term = -np.log(self.sigma)\n",
    "\n",
    "    def loglike(self, x, do_sum=True):\n",
    "        \n",
    "        dist_term = -(0.5)*((x - self.mu)/self.sigma)**2\n",
    "        if do_sum:\n",
    "            return (self.cte_term + self.det_sig_term + dist_term).sum()\n",
    "        else:\n",
    "            return (self.cte_term + self.det_sig_term + dist_term)\n",
    "    \n",
    "\n",
    "# TODO: adapt so can be done without sum\n",
    "class spike_slab_2GMM(object):\n",
    "    def __init__(self, mu1, mu2, sigma1, sigma2, pi):\n",
    "        \n",
    "        self.N1 = isotropic_gauss_prior(mu1, sigma1)\n",
    "        self.N2 = isotropic_gauss_prior(mu2, sigma2)\n",
    "        \n",
    "        self.pi1 = pi \n",
    "        self.pi2 = (1-pi)\n",
    "\n",
    "    def loglike(self, x):\n",
    "        \n",
    "        N1_ll = self.N1.loglike(x)\n",
    "        N2_ll = self.N2.loglike(x)\n",
    "        \n",
    "        # Numerical stability trick -> unnormalising logprobs will underflow otherwise\n",
    "        max_loglike = torch.max(N1_ll, N2_ll)\n",
    "        normalised_like = self.pi1 + torch.exp(N1_ll - max_loglike) + self.pi2 + torch.exp(N2_ll - max_loglike)\n",
    "        loglike = torch.log(normalised_like) + max_loglike\n",
    "    \n",
    "        return loglike\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weight layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def isnan(tensor):\n",
    "  # Gross: https://github.com/pytorch/pytorch/issues/4767\n",
    "    return (tensor != tensor)\n",
    "\n",
    "def hasnan(tensor):\n",
    "    return isnan(tensor).any()\n",
    "\n",
    "def isotropic_gauss_loglike(x, mu, sigma, do_sum=True):\n",
    "    cte_term = -(0.5)*np.log(2*np.pi)\n",
    "    det_sig_term = -torch.log(sigma)\n",
    "    inner = (x - mu)/sigma\n",
    "    dist_term = -(0.5)*(inner**2)\n",
    "    \n",
    "    if do_sum:\n",
    "        out = (cte_term + det_sig_term + dist_term).sum() # sum over all weights\n",
    "    else:\n",
    "        out = (cte_term + det_sig_term + dist_term)\n",
    "#     print(hasnan(x.data), hasnan(mu.data), hasnan(sigma.data), out)\n",
    "#     if isnan(out) or hasnan(out):\n",
    "#         print('NAaaanNNN')\n",
    "#         print('mu max', mu.max())\n",
    "#         print('mu min', mu.min())\n",
    "#         print('sigma max', sigma.max())\n",
    "#         print('sigma min', sigma.min())\n",
    "#         print('x max', x.max())\n",
    "#         print('x min', x.min())\n",
    "#         print((x - mu).max())\n",
    "#         print((x - mu).min())\n",
    "#         print(hasnan(inner))\n",
    "#         print(dist_term)\n",
    "#         print(hasnan(dist_term))\n",
    "    return out \n",
    "\n",
    "\n",
    "\n",
    "class BayesLinear_Normalq(nn.Module):\n",
    "    def __init__(self, n_in, n_out, prior_class):\n",
    "        super(BayesLinear_Normalq, self).__init__()\n",
    "        self.n_in = n_in\n",
    "        self.n_out = n_out\n",
    "        self.prior = prior_class\n",
    "        \n",
    "        # Learnable parameters\n",
    "        self.W_mu = nn.Parameter(torch.Tensor(self.n_in, self.n_out).uniform_(-0.2, 0.2))\n",
    "        self.W_p = nn.Parameter(torch.Tensor(self.n_in, self.n_out).uniform_(-3, -2))\n",
    "        \n",
    "        self.b_mu = nn.Parameter(torch.Tensor(self.n_out).uniform_(-0.2, 0.2))\n",
    "        self.b_p = nn.Parameter(torch.Tensor(self.n_out).uniform_(-3, -2))\n",
    "        \n",
    "       \n",
    "        self.lpw = 0\n",
    "        self.lqw = 0\n",
    "        \n",
    "                                   \n",
    "    def forward(self, X, sample=False):\n",
    "#         print(self.training)\n",
    "\n",
    "        if not self.training and not sample: # This is just a placeholder function\n",
    "            output = torch.mm(X, self.W_mu) + self.b_mu.expand(X.size()[0], self.n_out)\n",
    "            return output, 0, 0\n",
    "                                       \n",
    "        else:\n",
    "                              \n",
    "            # Tensor.new()  Constructs a new tensor of the same data type as self tensor. \n",
    "            # the same random sample is used for every element in the minibatch\n",
    "            eps_W = Variable(self.W_mu.data.new(self.W_mu.size()).normal_())\n",
    "            eps_b = Variable(self.b_mu.data.new(self.b_mu.size()).normal_())\n",
    "                                       \n",
    "            # sample parameters         \n",
    "            \n",
    "            std_w = 1e-6 + F.softplus(self.W_p, beta=1, threshold=20)\n",
    "            std_b = 1e-6 + F.softplus(self.b_p, beta=1, threshold=20)                      \n",
    "                                   \n",
    "            W = self.W_mu + 1 * std_w * eps_W\n",
    "            b = self.b_mu + 1 * std_b * eps_b          \n",
    "    \n",
    "            \n",
    "            output = torch.mm(X, W) + b.unsqueeze(0).expand(X.shape[0], -1) # (batch_size, n_output)\n",
    "            \n",
    "            lqw = isotropic_gauss_loglike(W, self.W_mu, std_w) + isotropic_gauss_loglike(b, self.b_mu, std_b)\n",
    "            lpw = self.prior.loglike(W) + self.prior.loglike(b)\n",
    "            return output, lqw, lpw\n",
    "\n",
    "            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quick weight sampling function for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sample_weights(W_mu, b_mu, W_p, b_p):\n",
    "    \n",
    "    eps_W = W_mu.data.new(W_mu.size()).normal_()\n",
    "    # sample parameters         \n",
    "    std_w = 1e-6 + F.softplus(W_p, beta=1, threshold=20)     \n",
    "    W = W_mu + 1 * std_w * eps_W\n",
    "    \n",
    "    if b_mu is not None:\n",
    "        std_b = 1e-6 + F.softplus(b_p, beta=1, threshold=20)  \n",
    "        eps_b = b_mu.data.new(b_mu.size()).normal_()\n",
    "        b = b_mu + 1 * std_b * eps_b\n",
    "    else:\n",
    "        b = None\n",
    "    \n",
    "    return W, b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Our models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class bayes_linear_2L(nn.Module):\n",
    "    def __init__(self, input_dim, output_dim):\n",
    "        super(bayes_linear_2L, self).__init__()\n",
    "        \n",
    "        n_hid = 1200\n",
    "#         prior_instance = isotropic_gauss_prior(mu=0, sigma=0.1)\n",
    "#         prior_instance = spike_slab_2GMM(mu1=0, mu2=0, sigma1=0.135, sigma2=0.001, pi=0.5)\n",
    "        self.prior_instance = isotropic_gauss_prior(mu=0, sigma=0.1)\n",
    "        \n",
    "        self.input_dim = input_dim\n",
    "        self.output_dim = output_dim\n",
    "        \n",
    "        self.bfc1 = BayesLinear_Normalq(input_dim, n_hid, self.prior_instance)\n",
    "        self.bfc2 = BayesLinear_Normalq(n_hid, n_hid, self.prior_instance)\n",
    "        self.bfc3 = BayesLinear_Normalq(n_hid, output_dim, self.prior_instance)\n",
    "        \n",
    "        # choose your non linearity\n",
    "        #self.act = nn.Tanh()\n",
    "        #self.act = nn.Sigmoid()\n",
    "        self.act = nn.ReLU(inplace=True)\n",
    "        #self.act = nn.ELU(inplace=True)\n",
    "        #self.act = nn.SELU(inplace=True)\n",
    "\n",
    "    def forward(self, x, sample=False):\n",
    "        \n",
    "        tlqw = 0\n",
    "        tlpw = 0\n",
    "        \n",
    "        x = x.view(-1, self.input_dim) # view(batch_size, input_dim)\n",
    "        # -----------------\n",
    "        x, lqw, lpw = self.bfc1(x, sample)\n",
    "        tlqw = tlqw + lqw\n",
    "        tlpw = tlpw + lpw\n",
    "        # -----------------\n",
    "        x = self.act(x)\n",
    "        # -----------------\n",
    "        x, lqw, lpw = self.bfc2(x, sample)\n",
    "        tlqw = tlqw + lqw\n",
    "        tlpw = tlpw + lpw\n",
    "        # -----------------\n",
    "        x = self.act(x)\n",
    "        # -----------------\n",
    "        y, lqw, lpw = self.bfc3(x, sample)\n",
    "        tlqw = tlqw + lqw\n",
    "        tlpw = tlpw + lpw\n",
    "        \n",
    "        return y, tlqw, tlpw\n",
    "    \n",
    "    def sample_predict(self, x, Nsamples):\n",
    "        \n",
    "        # Just copies type from x, initializes new vector\n",
    "        predictions = x.data.new(Nsamples, x.shape[0], self.output_dim)\n",
    "        tlqw_vec = np.zeros(Nsamples)\n",
    "        tlpw_vec = np.zeros(Nsamples)\n",
    "        \n",
    "        for i in range(Nsamples):\n",
    "            \n",
    "            y, tlqw, tlpw = self.forward(x, sample=True)\n",
    "            predictions[i] = y\n",
    "            tlqw_vec[i] = tlqw\n",
    "            tlpw_vec[i] = tlpw \n",
    "            \n",
    "        return predictions, tlqw_vec, tlpw_vec\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "import copy\n",
    "\n",
    "class Bayes_Net(BaseNet):\n",
    "    eps = 1e-6\n",
    "\n",
    "    def __init__(self, lr=1e-3, channels_in=3, side_in=28, cuda=True, classes=10, batch_size=128, Nbatches=0):\n",
    "        super(Bayes_Net, self).__init__()\n",
    "        cprint('y', ' Creating Net!! ')\n",
    "        self.lr = lr\n",
    "        self.schedule = None  # [] #[50,200,400,600]\n",
    "        self.cuda = cuda\n",
    "        self.channels_in = channels_in\n",
    "        self.classes = classes\n",
    "        self.batch_size = batch_size\n",
    "        self.Nbatches = Nbatches\n",
    "        self.side_in=side_in\n",
    "        self.create_net()\n",
    "        self.create_opt()\n",
    "        self.epoch = 0\n",
    "\n",
    "        self.test=False\n",
    "\n",
    "    def create_net(self):\n",
    "        torch.manual_seed(42)\n",
    "        if self.cuda:\n",
    "            torch.cuda.manual_seed(42)\n",
    "\n",
    "        self.model = bayes_linear_2L(input_dim=self.channels_in*self.side_in*self.side_in, output_dim=self.classes)\n",
    "        if self.cuda:\n",
    "            self.model.cuda()\n",
    "#             cudnn.benchmark = True\n",
    "\n",
    "        print('    Total params: %.2fM' % (self.get_nb_parameters() / 1000000.0))\n",
    "\n",
    "    def create_opt(self):\n",
    "#         self.optimizer = torch.optim.Adam(self.model.parameters(), lr=self.lr, betas=(0.9, 0.999), eps=1e-08,\n",
    "#                                           weight_decay=0)\n",
    "        self.optimizer = torch.optim.SGD(self.model.parameters(), lr=self.lr, momentum=0)\n",
    "\n",
    "    #         self.optimizer = torch.optim.SGD(self.model.parameters(), lr=self.lr, momentum=0.9)\n",
    "#         self.sched = torch.optim.lr_scheduler.StepLR(self.optimizer, step_size=1, gamma=10, last_epoch=-1)\n",
    "\n",
    "    def fit(self, x, y, samples=1):\n",
    "        x, y = to_variable(var=(x, y.long()), cuda=self.cuda)\n",
    "\n",
    "        self.optimizer.zero_grad()\n",
    "\n",
    "        if samples == 1:\n",
    "            out, tlqw, tlpw = self.model(x)\n",
    "            mlpdw = F.cross_entropy(out, y, reduction='sum')\n",
    "            Edkl = (tlqw - tlpw)/self.Nbatches\n",
    "            \n",
    "        elif samples > 1:\n",
    "            mlpdw_cum = 0\n",
    "            Edkl_cum = 0\n",
    "            \n",
    "            for i in range(samples):\n",
    "                out, tlqw, tlpw = self.model(x, sample=True)\n",
    "                mlpdw_i = F.cross_entropy(out, y, reduction='sum')\n",
    "                Edkl_i = (tlqw - tlpw)/self.Nbatches\n",
    "                mlpdw_cum = mlpdw_cum + mlpdw_i\n",
    "                Edkl_cum = Edkl_cum + Edkl_i \n",
    "            \n",
    "            mlpdw = mlpdw_cum/samples\n",
    "            Edkl = Edkl_cum/samples\n",
    "        \n",
    "        loss = Edkl + mlpdw\n",
    "        loss.backward()\n",
    "        self.optimizer.step()\n",
    "\n",
    "        # out: (batch_size, out_channels, out_caps_dims)\n",
    "        pred = out.data.max(dim=1, keepdim=False)[1]  # get the index of the max log-probability\n",
    "        err = pred.ne(y.data).sum()\n",
    "\n",
    "        return Edkl.data, mlpdw.data, err\n",
    "\n",
    "    def eval(self, x, y, train=False):\n",
    "        x, y = to_variable(var=(x, y.long()), cuda=self.cuda)\n",
    "\n",
    "        out, _, _ = self.model(x)\n",
    "\n",
    "        loss = F.cross_entropy(out, y, reduction='sum')\n",
    "\n",
    "        probs = F.softmax(out, dim=1).data.cpu()\n",
    "\n",
    "        pred = out.data.max(dim=1, keepdim=False)[1]  # get the index of the max log-probability\n",
    "        err = pred.ne(y.data).sum()\n",
    "\n",
    "        return loss.data, err, probs\n",
    "    \n",
    "    def sample_eval(self, x, y, Nsamples, logits=True, train=False):\n",
    "        x, y = to_variable(var=(x, y.long()), cuda=self.cuda)\n",
    "\n",
    "        out, _, _ = self.model.sample_predict(x, Nsamples)\n",
    "        \n",
    "        if logits:\n",
    "            mean_out = out.mean(dim=0, keepdim=False)\n",
    "            loss = F.cross_entropy(mean_out, y, reduction='sum')\n",
    "            probs = F.softmax(mean_out, dim=1).data.cpu()\n",
    "            \n",
    "        else:\n",
    "            mean_out =  F.softmax(out, dim=2).mean(dim=0, keepdim=False)\n",
    "            probs = mean_out.data.cpu()\n",
    "            \n",
    "            log_mean_probs_out = torch.log(mean_out)\n",
    "            loss = F.nll_loss(log_mean_probs_out, y, reduction='sum')\n",
    "\n",
    "        pred = mean_out.data.max(dim=1, keepdim=False)[1]  # get the index of the max log-probability\n",
    "        err = pred.ne(y.data).sum()\n",
    "\n",
    "        return loss.data, err, probs\n",
    "    \n",
    "    def all_sample_eval(self, x, y, Nsamples):\n",
    "        x, y = to_variable(var=(x, y.long()), cuda=self.cuda)\n",
    "\n",
    "        out, _, _ = self.model.sample_predict(x, Nsamples)\n",
    "        \n",
    "        prob_out =  F.softmax(out, dim=2)\n",
    "        prob_out = prob_out.data\n",
    "\n",
    "        return prob_out\n",
    "    \n",
    "    \n",
    "    def get_weight_samples(self, Nsamples=10):\n",
    "        state_dict = self.model.state_dict()\n",
    "        weight_vec = []\n",
    "        \n",
    "        for i in range(Nsamples):\n",
    "            previous_layer_name = ''\n",
    "            for key in state_dict.keys():\n",
    "                layer_name = key.split('.')[0]\n",
    "                if layer_name != previous_layer_name:\n",
    "                    previous_layer_name = layer_name\n",
    "\n",
    "                    W_mu = state_dict[layer_name+'.W_mu'].data\n",
    "                    W_p = state_dict[layer_name+'.W_p'].data\n",
    "\n",
    "    #                 b_mu = state_dict[layer_name+'.b_mu'].cpu().data\n",
    "    #                 b_p = state_dict[layer_name+'.b_p'].cpu().data\n",
    "\n",
    "                    W, b = sample_weights(W_mu=W_mu, b_mu=None, W_p=W_p, b_p=None)\n",
    "\n",
    "                    for weight in W.cpu().view(-1):\n",
    "                        weight_vec.append(weight)\n",
    "            \n",
    "        return np.array(weight_vec)\n",
    "    \n",
    "    def get_weight_SNR(self, thresh=None):\n",
    "        state_dict = self.model.state_dict()\n",
    "        weight_SNR_vec = []\n",
    "        \n",
    "        if thresh is not None:\n",
    "            mask_dict = {}\n",
    "        \n",
    "        previous_layer_name = ''\n",
    "        for key in state_dict.keys():\n",
    "            layer_name = key.split('.')[0]\n",
    "            if layer_name != previous_layer_name:\n",
    "                previous_layer_name = layer_name\n",
    "\n",
    "                W_mu = state_dict[layer_name+'.W_mu'].data\n",
    "                W_p = state_dict[layer_name+'.W_p'].data\n",
    "                sig_W = 1e-6 + F.softplus(W_p, beta=1, threshold=20)\n",
    "\n",
    "                b_mu = state_dict[layer_name+'.b_mu'].data\n",
    "                b_p = state_dict[layer_name+'.b_p'].data\n",
    "                sig_b = 1e-6 + F.softplus(b_p, beta=1, threshold=20)\n",
    "\n",
    "                W_snr = (torch.abs(W_mu)/sig_W)\n",
    "                b_snr = (torch.abs(b_mu)/sig_b)\n",
    "                \n",
    "                if thresh is not None:\n",
    "                    mask_dict[layer_name+'.W'] = W_snr > thresh\n",
    "                    mask_dict[layer_name+'.b'] = b_snr > thresh\n",
    "                    \n",
    "                else:\n",
    "                \n",
    "                    for weight_SNR in W_snr.cpu().view(-1):\n",
    "                        weight_SNR_vec.append(weight_SNR)\n",
    "\n",
    "                    for weight_SNR in b_snr.cpu().view(-1):\n",
    "                        weight_SNR_vec.append(weight_SNR)\n",
    "        \n",
    "        if thresh is not None:\n",
    "            return mask_dict\n",
    "        else:\n",
    "            return np.array(weight_SNR_vec)\n",
    "    \n",
    "    \n",
    "    def get_weight_KLD(self, Nsamples=20, thresh=None):\n",
    "        state_dict = self.model.state_dict()\n",
    "        weight_KLD_vec = []\n",
    "        \n",
    "        if thresh is not None:\n",
    "            mask_dict = {}\n",
    "        \n",
    "        previous_layer_name = ''\n",
    "        for key in state_dict.keys():\n",
    "            layer_name = key.split('.')[0]\n",
    "            if layer_name != previous_layer_name:\n",
    "                previous_layer_name = layer_name\n",
    "\n",
    "                W_mu = state_dict[layer_name+'.W_mu'].data\n",
    "                W_p = state_dict[layer_name+'.W_p'].data\n",
    "                b_mu = state_dict[layer_name+'.b_mu'].data\n",
    "                b_p = state_dict[layer_name+'.b_p'].data\n",
    "                \n",
    "                std_w = 1e-6 + F.softplus(W_p, beta=1, threshold=20)  \n",
    "                std_b = 1e-6 + F.softplus(b_p, beta=1, threshold=20)\n",
    "\n",
    "                KL_W = W_mu.new(W_mu.size()).zero_()\n",
    "                KL_b = b_mu.new(b_mu.size()).zero_()\n",
    "                for i in range(Nsamples):\n",
    "                    W, b = sample_weights(W_mu=W_mu, b_mu=b_mu, W_p=W_p, b_p=b_p)  \n",
    "                    # Note that this will currently not work with slab and spike prior\n",
    "                    KL_W += isotropic_gauss_loglike(W, W_mu, std_w, do_sum=False) - self.model.prior_instance.loglike(W, do_sum=False)\n",
    "                    KL_b += isotropic_gauss_loglike(b, b_mu, std_b, do_sum=False) - self.model.prior_instance.loglike(b, do_sum=False)\n",
    "\n",
    "                KL_W /= Nsamples\n",
    "                KL_b /= Nsamples\n",
    "                \n",
    "                if thresh is not None:\n",
    "                    mask_dict[layer_name+'.W'] = KL_W > thresh\n",
    "                    mask_dict[layer_name+'.b'] = KL_b > thresh\n",
    "                    \n",
    "                else:\n",
    "\n",
    "                    for weight_KLD in KL_W.cpu().view(-1):\n",
    "                        weight_KLD_vec.append(weight_KLD)\n",
    "\n",
    "                    for weight_KLD in KL_b.cpu().view(-1):\n",
    "                        weight_KLD_vec.append(weight_KLD)\n",
    "        \n",
    "        if thresh is not None:\n",
    "            return mask_dict\n",
    "        else:    \n",
    "            return np.array(weight_KLD_vec)\n",
    "        \n",
    "        \n",
    "    def mask_model(self, Nsamples=0, thresh=0):\n",
    "        '''\n",
    "        Nsamples is used to select SNR (0) or KLD (>0) based masking\n",
    "        '''\n",
    "        original_state_dict = copy.deepcopy(self.model.state_dict())\n",
    "        state_dict = self.model.state_dict()\n",
    "        \n",
    "        if Nsamples == 0:\n",
    "            mask_dict = self.get_weight_SNR(thresh=thresh)\n",
    "        else:\n",
    "            mask_dict = self.get_weight_KLD(Nsamples=Nsamples, thresh=thresh)\n",
    "        \n",
    "        n_unmasked = 0\n",
    "        \n",
    "        previous_layer_name = ''\n",
    "        for key in state_dict.keys():\n",
    "            layer_name = key.split('.')[0]\n",
    "            if layer_name != previous_layer_name:\n",
    "                previous_layer_name = layer_name\n",
    "                \n",
    "                state_dict[layer_name+'.W_mu'][1-mask_dict[layer_name+'.W']] = 0\n",
    "                state_dict[layer_name+'.W_p'][1-mask_dict[layer_name+'.W']] = -1000\n",
    "                \n",
    "                state_dict[layer_name+'.b_mu'][1-mask_dict[layer_name+'.b']] = 0\n",
    "                state_dict[layer_name+'.b_p'][1-mask_dict[layer_name+'.b']] = -1000\n",
    "                \n",
    "                \n",
    "                n_unmasked += mask_dict[layer_name+'.W'].sum()\n",
    "                n_unmasked += mask_dict[layer_name+'.b'].sum()\n",
    "                \n",
    "        return original_state_dict, n_unmasked\n",
    "            \n",
    "            \n",
    "            \n",
    "        \n",
    "            \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(net.model.state_dict()['bfc1.W_mu'].cpu().data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import copy\n",
    "# aaa = copy.deepcopy(net.model)\n",
    "# net.model = aaa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[36m\n",
      "Data:\u001b[0m\n",
      "\u001b[36m\n",
      "Network:\u001b[0m\n",
      "\u001b[36m\n",
      "Net:\u001b[0m\n",
      "\u001b[33m Creating Net!! \u001b[0m\n",
      "    Total params: 4.79M\n",
      "\u001b[36m\n",
      "Train:\u001b[0m\n",
      "  init cost variables:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:7: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.from_iter(generator)) or the python sum builtin instead.\n",
      "  import sys\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 0/160, Jtr_KL = 31.203477, Jtr_pred = 5.047227, err = 0.211350, \u001b[31m   time: 25.534850 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.308702, err = 0.087100\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_gaussian/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 1/160, Jtr_KL = 26.309973, Jtr_pred = 0.440938, err = 0.122050, \u001b[31m   time: 24.863601 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.221155, err = 0.062500\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_gaussian/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 2/160, Jtr_KL = 22.284633, Jtr_pred = 0.363394, err = 0.103333, \u001b[31m   time: 24.818471 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.209983, err = 0.061800\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_gaussian/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 3/160, Jtr_KL = 18.986415, Jtr_pred = 0.325342, err = 0.092433, \u001b[31m   time: 25.048214 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.178291, err = 0.052100\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_gaussian/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 4/160, Jtr_KL = 16.285972, Jtr_pred = 0.303208, err = 0.086600, \u001b[31m   time: 25.043441 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.162325, err = 0.047400\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_gaussian/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 5/160, Jtr_KL = 14.072903, Jtr_pred = 0.289486, err = 0.082750, \u001b[31m   time: 25.255916 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.154398, err = 0.045200\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_gaussian/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 6/160, Jtr_KL = 12.262292, Jtr_pred = 0.277555, err = 0.080533, \u001b[31m   time: 24.915884 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.154798, err = 0.045800\n",
      "\u001b[0m\n",
      "it 7/160, Jtr_KL = 10.779089, Jtr_pred = 0.272206, err = 0.077250, \u001b[31m   time: 25.170786 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.144157, err = 0.042900\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_gaussian/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 8/160, Jtr_KL = 9.563060, Jtr_pred = 0.266805, err = 0.076467, \u001b[31m   time: 25.305406 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.149234, err = 0.043600\n",
      "\u001b[0m\n",
      "it 9/160, Jtr_KL = 8.568611, Jtr_pred = 0.263681, err = 0.075183, \u001b[31m   time: 25.057328 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.141730, err = 0.041000\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_gaussian/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 10/160, Jtr_KL = 7.754061, Jtr_pred = 0.260034, err = 0.073917, \u001b[31m   time: 24.466184 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.137708, err = 0.041600\n",
      "\u001b[0m\n",
      "it 11/160, Jtr_KL = 7.085708, Jtr_pred = 0.254623, err = 0.072183, \u001b[31m   time: 24.724076 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.140612, err = 0.041900\n",
      "\u001b[0m\n",
      "it 12/160, Jtr_KL = 6.539221, Jtr_pred = 0.252951, err = 0.071933, \u001b[31m   time: 24.573428 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.135265, err = 0.038900\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_gaussian/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 13/160, Jtr_KL = 6.089686, Jtr_pred = 0.250326, err = 0.069983, \u001b[31m   time: 25.164389 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.127412, err = 0.038000\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_gaussian/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 14/160, Jtr_KL = 5.720603, Jtr_pred = 0.245963, err = 0.069100, \u001b[31m   time: 25.095530 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.129959, err = 0.037600\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_gaussian/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 15/160, Jtr_KL = 5.418055, Jtr_pred = 0.244868, err = 0.071583, \u001b[31m   time: 25.182010 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.119076, err = 0.035600\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_gaussian/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 16/160, Jtr_KL = 5.168071, Jtr_pred = 0.240780, err = 0.068533, \u001b[31m   time: 25.009224 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.114036, err = 0.034300\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_gaussian/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 17/160, Jtr_KL = 4.963361, Jtr_pred = 0.238585, err = 0.067100, \u001b[31m   time: 24.957605 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.116868, err = 0.034200\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_gaussian/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 18/160, Jtr_KL = 4.793924, Jtr_pred = 0.241288, err = 0.066850, \u001b[31m   time: 25.306613 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.128001, err = 0.037600\n",
      "\u001b[0m\n",
      "it 19/160, Jtr_KL = 4.654042, Jtr_pred = 0.235169, err = 0.066317, \u001b[31m   time: 24.957146 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.110889, err = 0.031800\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_gaussian/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 20/160, Jtr_KL = 4.538729, Jtr_pred = 0.234808, err = 0.065767, \u001b[31m   time: 25.452733 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.110620, err = 0.032800\n",
      "\u001b[0m\n",
      "it 21/160, Jtr_KL = 4.442723, Jtr_pred = 0.228583, err = 0.063783, \u001b[31m   time: 25.209089 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.106757, err = 0.030400\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_gaussian/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 22/160, Jtr_KL = 4.361597, Jtr_pred = 0.232141, err = 0.064467, \u001b[31m   time: 25.256530 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.109118, err = 0.032400\n",
      "\u001b[0m\n",
      "it 23/160, Jtr_KL = 4.294323, Jtr_pred = 0.226471, err = 0.062883, \u001b[31m   time: 25.235470 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.108737, err = 0.031600\n",
      "\u001b[0m\n",
      "it 24/160, Jtr_KL = 4.237731, Jtr_pred = 0.226450, err = 0.063167, \u001b[31m   time: 25.204455 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.115501, err = 0.034100\n",
      "\u001b[0m\n",
      "it 25/160, Jtr_KL = 4.190295, Jtr_pred = 0.222748, err = 0.061617, \u001b[31m   time: 25.252545 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.104298, err = 0.030800\n",
      "\u001b[0m\n",
      "it 26/160, Jtr_KL = 4.150486, Jtr_pred = 0.220599, err = 0.061867, \u001b[31m   time: 25.302165 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.106128, err = 0.032400\n",
      "\u001b[0m\n",
      "it 27/160, Jtr_KL = 4.114600, Jtr_pred = 0.218562, err = 0.063017, \u001b[31m   time: 25.193117 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.099437, err = 0.028400\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_gaussian/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 28/160, Jtr_KL = 4.084150, Jtr_pred = 0.218079, err = 0.061267, \u001b[31m   time: 25.451919 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.104728, err = 0.031400\n",
      "\u001b[0m\n",
      "it 29/160, Jtr_KL = 4.057307, Jtr_pred = 0.213912, err = 0.060350, \u001b[31m   time: 25.134244 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.103095, err = 0.031400\n",
      "\u001b[0m\n",
      "it 30/160, Jtr_KL = 4.034729, Jtr_pred = 0.215655, err = 0.059850, \u001b[31m   time: 25.246805 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.103768, err = 0.030800\n",
      "\u001b[0m\n",
      "it 31/160, Jtr_KL = 4.013955, Jtr_pred = 0.214184, err = 0.061633, \u001b[31m   time: 25.277499 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.104325, err = 0.031200\n",
      "\u001b[0m\n",
      "it 32/160, Jtr_KL = 3.996333, Jtr_pred = 0.210569, err = 0.059900, \u001b[31m   time: 25.235246 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.104465, err = 0.032000\n",
      "\u001b[0m\n",
      "it 33/160, Jtr_KL = 3.979039, Jtr_pred = 0.213297, err = 0.060817, \u001b[31m   time: 25.220598 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.101221, err = 0.030800\n",
      "\u001b[0m\n",
      "it 34/160, Jtr_KL = 3.964541, Jtr_pred = 0.209084, err = 0.059533, \u001b[31m   time: 25.188969 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.099522, err = 0.028800\n",
      "\u001b[0m\n",
      "it 35/160, Jtr_KL = 3.950069, Jtr_pred = 0.209363, err = 0.059417, \u001b[31m   time: 25.285341 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.103089, err = 0.029500\n",
      "\u001b[0m\n",
      "it 36/160, Jtr_KL = 3.937461, Jtr_pred = 0.204274, err = 0.058933, \u001b[31m   time: 24.901177 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.094600, err = 0.026400\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_gaussian/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 37/160, Jtr_KL = 3.924923, Jtr_pred = 0.206532, err = 0.059950, \u001b[31m   time: 24.813804 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.106182, err = 0.032200\n",
      "\u001b[0m\n",
      "it 38/160, Jtr_KL = 3.913281, Jtr_pred = 0.206573, err = 0.059350, \u001b[31m   time: 24.736313 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.103932, err = 0.031200\n",
      "\u001b[0m\n",
      "it 39/160, Jtr_KL = 3.902910, Jtr_pred = 0.203187, err = 0.058267, \u001b[31m   time: 24.737975 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.103827, err = 0.031300\n",
      "\u001b[0m\n",
      "it 40/160, Jtr_KL = 3.892021, Jtr_pred = 0.204169, err = 0.057083, \u001b[31m   time: 24.724653 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.100221, err = 0.029800\n",
      "\u001b[0m\n",
      "it 41/160, Jtr_KL = 3.882208, Jtr_pred = 0.202005, err = 0.058033, \u001b[31m   time: 25.496654 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.101310, err = 0.029500\n",
      "\u001b[0m\n",
      "it 42/160, Jtr_KL = 3.872602, Jtr_pred = 0.201906, err = 0.057167, \u001b[31m   time: 25.342271 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.097063, err = 0.028800\n",
      "\u001b[0m\n",
      "it 43/160, Jtr_KL = 3.862915, Jtr_pred = 0.200171, err = 0.057700, \u001b[31m   time: 25.131813 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.102266, err = 0.030700\n",
      "\u001b[0m\n",
      "it 44/160, Jtr_KL = 3.853529, Jtr_pred = 0.199507, err = 0.057350, \u001b[31m   time: 25.020777 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.097834, err = 0.029900\n",
      "\u001b[0m\n",
      "it 45/160, Jtr_KL = 3.844831, Jtr_pred = 0.199689, err = 0.057150, \u001b[31m   time: 25.048516 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.098606, err = 0.029200\n",
      "\u001b[0m\n",
      "it 46/160, Jtr_KL = 3.835970, Jtr_pred = 0.200608, err = 0.055533, \u001b[31m   time: 24.967283 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.093654, err = 0.028100\n",
      "\u001b[0m\n",
      "it 47/160, Jtr_KL = 3.827489, Jtr_pred = 0.199029, err = 0.057050, \u001b[31m   time: 25.021435 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.098022, err = 0.030100\n",
      "\u001b[0m\n",
      "it 48/160, Jtr_KL = 3.818752, Jtr_pred = 0.197084, err = 0.057333, \u001b[31m   time: 25.158480 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.090593, err = 0.026500\n",
      "\u001b[0m\n",
      "it 49/160, Jtr_KL = 3.809851, Jtr_pred = 0.196716, err = 0.056083, \u001b[31m   time: 25.171614 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.101564, err = 0.031700\n",
      "\u001b[0m\n",
      "it 50/160, Jtr_KL = 3.801468, Jtr_pred = 0.194888, err = 0.056750, \u001b[31m   time: 25.256988 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.104773, err = 0.031700\n",
      "\u001b[0m\n",
      "it 51/160, Jtr_KL = 3.792775, Jtr_pred = 0.193597, err = 0.054717, \u001b[31m   time: 25.345147 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.104421, err = 0.031500\n",
      "\u001b[0m\n",
      "it 52/160, Jtr_KL = 3.784560, Jtr_pred = 0.194050, err = 0.055067, \u001b[31m   time: 25.483088 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.097017, err = 0.028600\n",
      "\u001b[0m\n",
      "it 53/160, Jtr_KL = 3.775659, Jtr_pred = 0.195481, err = 0.055167, \u001b[31m   time: 24.916305 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.097961, err = 0.029700\n",
      "\u001b[0m\n",
      "it 54/160, Jtr_KL = 3.768038, Jtr_pred = 0.193388, err = 0.056550, \u001b[31m   time: 24.766312 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.092968, err = 0.028500\n",
      "\u001b[0m\n",
      "it 55/160, Jtr_KL = 3.759976, Jtr_pred = 0.192462, err = 0.055167, \u001b[31m   time: 25.007464 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.092626, err = 0.028200\n",
      "\u001b[0m\n",
      "it 56/160, Jtr_KL = 3.751935, Jtr_pred = 0.191353, err = 0.054433, \u001b[31m   time: 25.013842 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.090496, err = 0.027600\n",
      "\u001b[0m\n",
      "it 57/160, Jtr_KL = 3.743690, Jtr_pred = 0.192289, err = 0.056550, \u001b[31m   time: 25.336342 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.094885, err = 0.028800\n",
      "\u001b[0m\n",
      "it 58/160, Jtr_KL = 3.735031, Jtr_pred = 0.190147, err = 0.054417, \u001b[31m   time: 25.365644 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.096238, err = 0.029000\n",
      "\u001b[0m\n",
      "it 59/160, Jtr_KL = 3.727387, Jtr_pred = 0.189888, err = 0.055583, \u001b[31m   time: 25.299483 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.103048, err = 0.032800\n",
      "\u001b[0m\n",
      "it 60/160, Jtr_KL = 3.719347, Jtr_pred = 0.189976, err = 0.054850, \u001b[31m   time: 25.214326 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.096267, err = 0.029900\n",
      "\u001b[0m\n",
      "it 61/160, Jtr_KL = 3.711277, Jtr_pred = 0.188996, err = 0.055417, \u001b[31m   time: 25.216311 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.093925, err = 0.029400\n",
      "\u001b[0m\n",
      "it 62/160, Jtr_KL = 3.702964, Jtr_pred = 0.189656, err = 0.056017, \u001b[31m   time: 25.111697 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.095260, err = 0.028000\n",
      "\u001b[0m\n",
      "it 63/160, Jtr_KL = 3.695614, Jtr_pred = 0.189251, err = 0.055850, \u001b[31m   time: 25.299507 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.092655, err = 0.028200\n",
      "\u001b[0m\n",
      "it 64/160, Jtr_KL = 3.687799, Jtr_pred = 0.186620, err = 0.055000, \u001b[31m   time: 25.317189 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.095614, err = 0.029600\n",
      "\u001b[0m\n",
      "it 65/160, Jtr_KL = 3.679891, Jtr_pred = 0.187547, err = 0.054750, \u001b[31m   time: 25.389989 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.089567, err = 0.027800\n",
      "\u001b[0m\n",
      "it 66/160, Jtr_KL = 3.671493, Jtr_pred = 0.186949, err = 0.053483, \u001b[31m   time: 25.262366 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.095535, err = 0.029300\n",
      "\u001b[0m\n",
      "it 67/160, Jtr_KL = 3.664105, Jtr_pred = 0.186962, err = 0.054967, \u001b[31m   time: 25.406659 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.092625, err = 0.027100\n",
      "\u001b[0m\n",
      "it 68/160, Jtr_KL = 3.656811, Jtr_pred = 0.184688, err = 0.053850, \u001b[31m   time: 25.515396 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.095264, err = 0.027900\n",
      "\u001b[0m\n",
      "it 69/160, Jtr_KL = 3.648485, Jtr_pred = 0.184556, err = 0.053017, \u001b[31m   time: 25.200389 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.093437, err = 0.029400\n",
      "\u001b[0m\n",
      "it 70/160, Jtr_KL = 3.640578, Jtr_pred = 0.184788, err = 0.053917, \u001b[31m   time: 25.253717 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.096591, err = 0.028900\n",
      "\u001b[0m\n",
      "it 71/160, Jtr_KL = 3.632538, Jtr_pred = 0.184005, err = 0.054533, \u001b[31m   time: 25.163854 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.097501, err = 0.028100\n",
      "\u001b[0m\n",
      "it 72/160, Jtr_KL = 3.625078, Jtr_pred = 0.182835, err = 0.053133, \u001b[31m   time: 25.237614 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.090539, err = 0.027000\n",
      "\u001b[0m\n",
      "it 73/160, Jtr_KL = 3.617027, Jtr_pred = 0.183764, err = 0.054000, \u001b[31m   time: 25.276243 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.092402, err = 0.027700\n",
      "\u001b[0m\n",
      "it 74/160, Jtr_KL = 3.609059, Jtr_pred = 0.183258, err = 0.052583, \u001b[31m   time: 25.185251 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.106377, err = 0.032800\n",
      "\u001b[0m\n",
      "it 75/160, Jtr_KL = 3.601674, Jtr_pred = 0.182767, err = 0.052917, \u001b[31m   time: 25.062904 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.095896, err = 0.029700\n",
      "\u001b[0m\n",
      "it 76/160, Jtr_KL = 3.593754, Jtr_pred = 0.180841, err = 0.053767, \u001b[31m   time: 25.273851 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.102092, err = 0.032400\n",
      "\u001b[0m\n",
      "it 77/160, Jtr_KL = 3.585389, Jtr_pred = 0.179127, err = 0.050950, \u001b[31m   time: 25.384251 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.100099, err = 0.030600\n",
      "\u001b[0m\n",
      "it 78/160, Jtr_KL = 3.577785, Jtr_pred = 0.181796, err = 0.053233, \u001b[31m   time: 25.784715 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.088213, err = 0.027000\n",
      "\u001b[0m\n",
      "it 79/160, Jtr_KL = 3.570598, Jtr_pred = 0.183049, err = 0.053917, \u001b[31m   time: 24.786300 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.095158, err = 0.027500\n",
      "\u001b[0m\n",
      "it 80/160, Jtr_KL = 3.563423, Jtr_pred = 0.181653, err = 0.053400, \u001b[31m   time: 24.723278 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.087080, err = 0.025400\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_gaussian/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 81/160, Jtr_KL = 3.555458, Jtr_pred = 0.179679, err = 0.051233, \u001b[31m   time: 25.251296 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.092737, err = 0.026900\n",
      "\u001b[0m\n",
      "it 82/160, Jtr_KL = 3.548224, Jtr_pred = 0.179153, err = 0.052450, \u001b[31m   time: 25.227121 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.086572, err = 0.026500\n",
      "\u001b[0m\n",
      "it 83/160, Jtr_KL = 3.541361, Jtr_pred = 0.182278, err = 0.053517, \u001b[31m   time: 25.105680 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.098819, err = 0.030300\n",
      "\u001b[0m\n",
      "it 84/160, Jtr_KL = 3.533343, Jtr_pred = 0.181874, err = 0.053217, \u001b[31m   time: 25.148346 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.096526, err = 0.028300\n",
      "\u001b[0m\n",
      "it 85/160, Jtr_KL = 3.526528, Jtr_pred = 0.179168, err = 0.051783, \u001b[31m   time: 25.121983 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.095660, err = 0.028200\n",
      "\u001b[0m\n",
      "it 86/160, Jtr_KL = 3.518940, Jtr_pred = 0.176970, err = 0.052050, \u001b[31m   time: 25.334440 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.090357, err = 0.027500\n",
      "\u001b[0m\n",
      "it 87/160, Jtr_KL = 3.510732, Jtr_pred = 0.178141, err = 0.053117, \u001b[31m   time: 25.263016 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.097733, err = 0.028600\n",
      "\u001b[0m\n",
      "it 88/160, Jtr_KL = 3.503224, Jtr_pred = 0.177243, err = 0.052017, \u001b[31m   time: 25.486880 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.097206, err = 0.030300\n",
      "\u001b[0m\n",
      "it 89/160, Jtr_KL = 3.496043, Jtr_pred = 0.177112, err = 0.052117, \u001b[31m   time: 25.116659 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.090704, err = 0.026800\n",
      "\u001b[0m\n",
      "it 90/160, Jtr_KL = 3.488512, Jtr_pred = 0.175984, err = 0.051450, \u001b[31m   time: 24.062009 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.106548, err = 0.032700\n",
      "\u001b[0m\n",
      "it 91/160, Jtr_KL = 3.480957, Jtr_pred = 0.176274, err = 0.051833, \u001b[31m   time: 25.100954 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.091828, err = 0.027400\n",
      "\u001b[0m\n",
      "it 92/160, Jtr_KL = 3.473210, Jtr_pred = 0.179205, err = 0.052517, \u001b[31m   time: 26.202980 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.094284, err = 0.029300\n",
      "\u001b[0m\n",
      "it 93/160, Jtr_KL = 3.466288, Jtr_pred = 0.178042, err = 0.052300, \u001b[31m   time: 26.469096 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.090760, err = 0.027300\n",
      "\u001b[0m\n",
      "it 94/160, Jtr_KL = 3.458960, Jtr_pred = 0.175805, err = 0.050600, \u001b[31m   time: 24.923244 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.094609, err = 0.027100\n",
      "\u001b[0m\n",
      "it 95/160, Jtr_KL = 3.451597, Jtr_pred = 0.177992, err = 0.052817, \u001b[31m   time: 25.151905 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.087638, err = 0.026200\n",
      "\u001b[0m\n",
      "it 96/160, Jtr_KL = 3.443993, Jtr_pred = 0.173836, err = 0.051500, \u001b[31m   time: 25.110015 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.090845, err = 0.028100\n",
      "\u001b[0m\n",
      "it 97/160, Jtr_KL = 3.436259, Jtr_pred = 0.174486, err = 0.051283, \u001b[31m   time: 25.266258 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.089817, err = 0.025800\n",
      "\u001b[0m\n",
      "it 98/160, Jtr_KL = 3.429357, Jtr_pred = 0.177283, err = 0.052300, \u001b[31m   time: 25.278207 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.090289, err = 0.026700\n",
      "\u001b[0m\n",
      "it 99/160, Jtr_KL = 3.423444, Jtr_pred = 0.176022, err = 0.054083, \u001b[31m   time: 25.506589 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.088982, err = 0.028000\n",
      "\u001b[0m\n",
      "it 100/160, Jtr_KL = 3.415345, Jtr_pred = 0.174488, err = 0.050783, \u001b[31m   time: 25.138216 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.089449, err = 0.026100\n",
      "\u001b[0m\n",
      "it 101/160, Jtr_KL = 3.407302, Jtr_pred = 0.175197, err = 0.051950, \u001b[31m   time: 25.170779 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.091004, err = 0.027700\n",
      "\u001b[0m\n",
      "it 102/160, Jtr_KL = 3.400056, Jtr_pred = 0.174867, err = 0.050600, \u001b[31m   time: 25.146926 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.090691, err = 0.028300\n",
      "\u001b[0m\n",
      "it 103/160, Jtr_KL = 3.393468, Jtr_pred = 0.173132, err = 0.050533, \u001b[31m   time: 25.179248 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.095570, err = 0.029300\n",
      "\u001b[0m\n",
      "it 104/160, Jtr_KL = 3.385944, Jtr_pred = 0.173296, err = 0.051067, \u001b[31m   time: 25.172060 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.102097, err = 0.031200\n",
      "\u001b[0m\n",
      "it 105/160, Jtr_KL = 3.378493, Jtr_pred = 0.173748, err = 0.050867, \u001b[31m   time: 25.316681 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.091069, err = 0.028100\n",
      "\u001b[0m\n",
      "it 106/160, Jtr_KL = 3.371624, Jtr_pred = 0.174969, err = 0.050467, \u001b[31m   time: 25.157447 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.082111, err = 0.023600\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_gaussian/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 107/160, Jtr_KL = 3.364783, Jtr_pred = 0.172493, err = 0.051233, \u001b[31m   time: 25.403819 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.086922, err = 0.026100\n",
      "\u001b[0m\n",
      "it 108/160, Jtr_KL = 3.357741, Jtr_pred = 0.174143, err = 0.051317, \u001b[31m   time: 25.114247 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.095302, err = 0.029400\n",
      "\u001b[0m\n",
      "it 109/160, Jtr_KL = 3.350489, Jtr_pred = 0.171011, err = 0.052383, \u001b[31m   time: 25.321529 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.087803, err = 0.026300\n",
      "\u001b[0m\n",
      "it 110/160, Jtr_KL = 3.343105, Jtr_pred = 0.171931, err = 0.051567, \u001b[31m   time: 25.231597 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.092760, err = 0.026700\n",
      "\u001b[0m\n",
      "it 111/160, Jtr_KL = 3.336057, Jtr_pred = 0.173082, err = 0.050850, \u001b[31m   time: 25.456269 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.098666, err = 0.030200\n",
      "\u001b[0m\n",
      "it 112/160, Jtr_KL = 3.329438, Jtr_pred = 0.173473, err = 0.050617, \u001b[31m   time: 25.430656 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.089265, err = 0.026600\n",
      "\u001b[0m\n",
      "it 113/160, Jtr_KL = 3.322029, Jtr_pred = 0.172817, err = 0.050750, \u001b[31m   time: 25.277911 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.084534, err = 0.024700\n",
      "\u001b[0m\n",
      "it 114/160, Jtr_KL = 3.316046, Jtr_pred = 0.172012, err = 0.050517, \u001b[31m   time: 24.887822 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.091980, err = 0.027900\n",
      "\u001b[0m\n",
      "it 115/160, Jtr_KL = 3.308509, Jtr_pred = 0.171508, err = 0.050233, \u001b[31m   time: 25.215083 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.088678, err = 0.025500\n",
      "\u001b[0m\n",
      "it 116/160, Jtr_KL = 3.301330, Jtr_pred = 0.170547, err = 0.051067, \u001b[31m   time: 25.230106 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.096548, err = 0.028100\n",
      "\u001b[0m\n",
      "it 117/160, Jtr_KL = 3.294395, Jtr_pred = 0.168230, err = 0.049600, \u001b[31m   time: 25.006025 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.088001, err = 0.026900\n",
      "\u001b[0m\n",
      "it 118/160, Jtr_KL = 3.286838, Jtr_pred = 0.170109, err = 0.050733, \u001b[31m   time: 25.072638 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.092240, err = 0.028200\n",
      "\u001b[0m\n",
      "it 119/160, Jtr_KL = 3.280875, Jtr_pred = 0.169122, err = 0.050800, \u001b[31m   time: 25.288457 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.093862, err = 0.028100\n",
      "\u001b[0m\n",
      "it 120/160, Jtr_KL = 3.273869, Jtr_pred = 0.170438, err = 0.050767, \u001b[31m   time: 25.429839 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.085500, err = 0.025800\n",
      "\u001b[0m\n",
      "it 121/160, Jtr_KL = 3.267064, Jtr_pred = 0.171495, err = 0.050167, \u001b[31m   time: 25.076461 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.095378, err = 0.028700\n",
      "\u001b[0m\n",
      "it 122/160, Jtr_KL = 3.260512, Jtr_pred = 0.169476, err = 0.050983, \u001b[31m   time: 24.717634 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.093263, err = 0.026400\n",
      "\u001b[0m\n",
      "it 123/160, Jtr_KL = 3.253824, Jtr_pred = 0.168928, err = 0.049883, \u001b[31m   time: 24.913537 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.091029, err = 0.025000\n",
      "\u001b[0m\n",
      "it 124/160, Jtr_KL = 3.247407, Jtr_pred = 0.170001, err = 0.050233, \u001b[31m   time: 24.886062 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.093030, err = 0.027600\n",
      "\u001b[0m\n",
      "it 125/160, Jtr_KL = 3.240397, Jtr_pred = 0.170464, err = 0.050667, \u001b[31m   time: 24.703099 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.086845, err = 0.026700\n",
      "\u001b[0m\n",
      "it 126/160, Jtr_KL = 3.233396, Jtr_pred = 0.170717, err = 0.050150, \u001b[31m   time: 24.757391 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.097741, err = 0.029400\n",
      "\u001b[0m\n",
      "it 127/160, Jtr_KL = 3.226402, Jtr_pred = 0.168718, err = 0.049883, \u001b[31m   time: 25.159748 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.086091, err = 0.025700\n",
      "\u001b[0m\n",
      "it 128/160, Jtr_KL = 3.219231, Jtr_pred = 0.167224, err = 0.049883, \u001b[31m   time: 25.391303 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.086319, err = 0.025800\n",
      "\u001b[0m\n",
      "it 129/160, Jtr_KL = 3.212277, Jtr_pred = 0.170188, err = 0.049983, \u001b[31m   time: 25.351955 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.089793, err = 0.028400\n",
      "\u001b[0m\n",
      "it 130/160, Jtr_KL = 3.205334, Jtr_pred = 0.168193, err = 0.050683, \u001b[31m   time: 25.182363 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.084545, err = 0.025500\n",
      "\u001b[0m\n",
      "it 131/160, Jtr_KL = 3.198617, Jtr_pred = 0.169167, err = 0.051083, \u001b[31m   time: 25.233893 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.087252, err = 0.027600\n",
      "\u001b[0m\n",
      "it 132/160, Jtr_KL = 3.191724, Jtr_pred = 0.169478, err = 0.050633, \u001b[31m   time: 25.262102 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.086214, err = 0.025800\n",
      "\u001b[0m\n",
      "it 133/160, Jtr_KL = 3.185324, Jtr_pred = 0.169551, err = 0.050950, \u001b[31m   time: 25.164643 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.090572, err = 0.025800\n",
      "\u001b[0m\n",
      "it 134/160, Jtr_KL = 3.178922, Jtr_pred = 0.168172, err = 0.050717, \u001b[31m   time: 24.785065 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.088975, err = 0.025700\n",
      "\u001b[0m\n",
      "it 135/160, Jtr_KL = 3.172168, Jtr_pred = 0.166659, err = 0.049400, \u001b[31m   time: 24.846340 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.090103, err = 0.027800\n",
      "\u001b[0m\n",
      "it 136/160, Jtr_KL = 3.165762, Jtr_pred = 0.166221, err = 0.050267, \u001b[31m   time: 25.015166 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.091959, err = 0.026500\n",
      "\u001b[0m\n",
      "it 137/160, Jtr_KL = 3.158918, Jtr_pred = 0.168789, err = 0.050467, \u001b[31m   time: 24.865672 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.091546, err = 0.028400\n",
      "\u001b[0m\n",
      "it 138/160, Jtr_KL = 3.152785, Jtr_pred = 0.167241, err = 0.049950, \u001b[31m   time: 24.915004 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.093218, err = 0.027900\n",
      "\u001b[0m\n",
      "it 139/160, Jtr_KL = 3.145529, Jtr_pred = 0.166766, err = 0.049667, \u001b[31m   time: 24.823437 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.099863, err = 0.028200\n",
      "\u001b[0m\n",
      "it 140/160, Jtr_KL = 3.138903, Jtr_pred = 0.167655, err = 0.049700, \u001b[31m   time: 25.300917 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.092453, err = 0.028100\n",
      "\u001b[0m\n",
      "it 141/160, Jtr_KL = 3.132442, Jtr_pred = 0.165637, err = 0.048867, \u001b[31m   time: 25.263712 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.087626, err = 0.026100\n",
      "\u001b[0m\n",
      "it 142/160, Jtr_KL = 3.125870, Jtr_pred = 0.166637, err = 0.049950, \u001b[31m   time: 25.277972 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.095704, err = 0.029000\n",
      "\u001b[0m\n",
      "it 143/160, Jtr_KL = 3.119618, Jtr_pred = 0.167320, err = 0.050133, \u001b[31m   time: 25.260868 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.097053, err = 0.029900\n",
      "\u001b[0m\n",
      "it 144/160, Jtr_KL = 3.113229, Jtr_pred = 0.167912, err = 0.049033, \u001b[31m   time: 25.326915 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.091017, err = 0.027800\n",
      "\u001b[0m\n",
      "it 145/160, Jtr_KL = 3.106460, Jtr_pred = 0.164841, err = 0.049167, \u001b[31m   time: 25.521901 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.087713, err = 0.026400\n",
      "\u001b[0m\n",
      "it 146/160, Jtr_KL = 3.099677, Jtr_pred = 0.165548, err = 0.050617, \u001b[31m   time: 25.380713 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.100191, err = 0.032700\n",
      "\u001b[0m\n",
      "it 147/160, Jtr_KL = 3.093239, Jtr_pred = 0.168017, err = 0.049250, \u001b[31m   time: 25.373417 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.095369, err = 0.028400\n",
      "\u001b[0m\n",
      "it 148/160, Jtr_KL = 3.086769, Jtr_pred = 0.166122, err = 0.049183, \u001b[31m   time: 25.115491 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.086870, err = 0.024800\n",
      "\u001b[0m\n",
      "it 149/160, Jtr_KL = 3.080562, Jtr_pred = 0.164468, err = 0.049183, \u001b[31m   time: 25.107683 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.088803, err = 0.026200\n",
      "\u001b[0m\n",
      "it 150/160, Jtr_KL = 3.074040, Jtr_pred = 0.164248, err = 0.049050, \u001b[31m   time: 25.125185 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.085382, err = 0.025500\n",
      "\u001b[0m\n",
      "it 151/160, Jtr_KL = 3.067458, Jtr_pred = 0.164323, err = 0.049433, \u001b[31m   time: 25.228058 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.088632, err = 0.026300\n",
      "\u001b[0m\n",
      "it 152/160, Jtr_KL = 3.059736, Jtr_pred = 0.166320, err = 0.049133, \u001b[31m   time: 25.267589 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.084687, err = 0.025400\n",
      "\u001b[0m\n",
      "it 153/160, Jtr_KL = 3.054082, Jtr_pred = 0.167155, err = 0.048867, \u001b[31m   time: 25.119623 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.089251, err = 0.027100\n",
      "\u001b[0m\n",
      "it 154/160, Jtr_KL = 3.047876, Jtr_pred = 0.166020, err = 0.048983, \u001b[31m   time: 25.057522 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.088773, err = 0.027100\n",
      "\u001b[0m\n",
      "it 155/160, Jtr_KL = 3.040771, Jtr_pred = 0.166376, err = 0.049583, \u001b[31m   time: 25.229116 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.088532, err = 0.026500\n",
      "\u001b[0m\n",
      "it 156/160, Jtr_KL = 3.035310, Jtr_pred = 0.166003, err = 0.050250, \u001b[31m   time: 24.865616 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.087746, err = 0.027100\n",
      "\u001b[0m\n",
      "it 157/160, Jtr_KL = 3.028767, Jtr_pred = 0.163239, err = 0.048950, \u001b[31m   time: 25.191079 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.084885, err = 0.025500\n",
      "\u001b[0m\n",
      "it 158/160, Jtr_KL = 3.022364, Jtr_pred = 0.165610, err = 0.050267, \u001b[31m   time: 25.276077 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.086661, err = 0.025000\n",
      "\u001b[0m\n",
      "it 159/160, Jtr_KL = 3.016586, Jtr_pred = 0.164682, err = 0.049683, \u001b[31m   time: 25.151171 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.085881, err = 0.026000\n",
      "\u001b[0m\n",
      "\u001b[31m   average time: 26.076770 seconds\n",
      "\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_gaussian/theta_last.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[36m\n",
      "RESULTS:\u001b[0m\n",
      "  cost_dev: 0.082111 (cost_train 0.163239)\n",
      "  err_dev: 0.023600\n",
      "  nb_parameters: 4790420 (4.57MB)\n",
      "  time_per_it: 26.076770s\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "from __future__ import division\n",
    "import time\n",
    "import torch.utils.data\n",
    "from torchvision import transforms, datasets\n",
    "import matplotlib\n",
    "import copy\n",
    "\n",
    "\n",
    "models_dir = 'models_weight_uncertainty_MC_MNIST_gaussian'\n",
    "results_dir = 'results_weight_uncertainty_MC_MNIST_gaussian'\n",
    "\n",
    "mkdir(models_dir)\n",
    "mkdir(results_dir)\n",
    "# ------------------------------------------------------------------------------------------------------\n",
    "# train config\n",
    "NTrainPointsMNIST = 60000\n",
    "batch_size = 100\n",
    "nb_epochs = 160\n",
    "log_interval = 1\n",
    "\n",
    "savemodel_its = [20, 50, 80, 120]\n",
    "save_dicts = []\n",
    "\n",
    "# ------------------------------------------------------------------------------------------------------\n",
    "# dataset\n",
    "cprint('c', '\\nData:')\n",
    "\n",
    "\n",
    "# load data\n",
    "\n",
    "# data augmentation\n",
    "transform_train = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=(0.1307,), std=(0.3081,))\n",
    "])\n",
    "\n",
    "transform_test = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=(0.1307,), std=(0.3081,))\n",
    "])\n",
    "\n",
    "use_cuda = torch.cuda.is_available()\n",
    "\n",
    "trainset = datasets.MNIST(root='../data', train=True, download=True, transform=transform_train)\n",
    "valset = datasets.MNIST(root='../data', train=False, download=True, transform=transform_test)\n",
    "\n",
    "if use_cuda:\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=True, num_workers=3)\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=3)\n",
    "\n",
    "else:\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=False,\n",
    "                                              num_workers=3)\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=False,\n",
    "                                            num_workers=3)\n",
    "\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# net dims\n",
    "cprint('c', '\\nNetwork:')\n",
    "\n",
    "lr = 1e-3\n",
    "nsamples = 3\n",
    "########################################################################################\n",
    "net = Bayes_Net(lr=lr, channels_in=1, side_in=28, cuda=use_cuda, classes=10, batch_size=batch_size,\n",
    "          Nbatches=(NTrainPointsMNIST/batch_size))\n",
    "\n",
    "epoch = 0\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# train\n",
    "cprint('c', '\\nTrain:')\n",
    "\n",
    "print('  init cost variables:')\n",
    "kl_cost_train = np.zeros(nb_epochs)\n",
    "pred_cost_train = np.zeros(nb_epochs)\n",
    "err_train = np.zeros(nb_epochs)\n",
    "\n",
    "cost_dev = np.zeros(nb_epochs)\n",
    "err_dev = np.zeros(nb_epochs)\n",
    "# best_cost = np.inf\n",
    "best_err = np.inf\n",
    "\n",
    "\n",
    "nb_its_dev = 1\n",
    "\n",
    "tic0 = time.time()\n",
    "for i in range(epoch, nb_epochs):\n",
    "    \n",
    "#     if i in [1]:\n",
    "#         print('updating lr')\n",
    "#         net.sched.step()\n",
    "    \n",
    "    net.set_mode_train(True)\n",
    "\n",
    "    tic = time.time()\n",
    "    nb_samples = 0\n",
    "\n",
    "    for x, y in trainloader:\n",
    "        cost_dkl, cost_pred, err = net.fit(x, y, samples=nsamples)\n",
    "\n",
    "        err_train[i] += err\n",
    "        kl_cost_train[i] += cost_dkl\n",
    "        pred_cost_train[i] += cost_pred\n",
    "        nb_samples += len(x)\n",
    "\n",
    "    kl_cost_train[i] /= nb_samples\n",
    "    pred_cost_train[i] /= nb_samples\n",
    "    err_train[i] /= nb_samples\n",
    "\n",
    "    toc = time.time()\n",
    "    net.epoch = i\n",
    "    # ---- print\n",
    "    print(\"it %d/%d, Jtr_KL = %f, Jtr_pred = %f, err = %f, \" % (i, nb_epochs, kl_cost_train[i], pred_cost_train[i], err_train[i]), end=\"\")\n",
    "    cprint('r', '   time: %f seconds\\n' % (toc - tic))\n",
    "\n",
    "    # Save state dict\n",
    "    \n",
    "    if i in savemodel_its:\n",
    "        save_dicts.append(copy.deepcopy(net.model.state_dict()))\n",
    "    \n",
    "    # ---- dev\n",
    "    if i % nb_its_dev == 0:\n",
    "        net.set_mode_train(False)\n",
    "        nb_samples = 0\n",
    "        for j, (x, y) in enumerate(valloader):\n",
    "\n",
    "            cost, err, probs = net.eval(x, y)\n",
    "\n",
    "            cost_dev[i] += cost\n",
    "            err_dev[i] += err\n",
    "            nb_samples += len(x)\n",
    "\n",
    "        cost_dev[i] /= nb_samples\n",
    "        err_dev[i] /= nb_samples\n",
    "\n",
    "        cprint('g', '    Jdev = %f, err = %f\\n' % (cost_dev[i], err_dev[i]))\n",
    "\n",
    "        if err_dev[i] < best_err:\n",
    "            best_err = err_dev[i]\n",
    "            cprint('b', 'best test error')\n",
    "            net.save(models_dir+'/theta_best.dat')\n",
    "\n",
    "toc0 = time.time()\n",
    "runtime_per_it = (toc0 - tic0) / float(nb_epochs)\n",
    "cprint('r', '   average time: %f seconds\\n' % runtime_per_it)\n",
    "\n",
    "net.save(models_dir+'/theta_last.dat')\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# results\n",
    "cprint('c', '\\nRESULTS:')\n",
    "nb_parameters = net.get_nb_parameters()\n",
    "best_cost_dev = np.min(cost_dev)\n",
    "best_cost_train = np.min(pred_cost_train)\n",
    "err_dev_min = err_dev[::nb_its_dev].min()\n",
    "\n",
    "print('  cost_dev: %f (cost_train %f)' % (best_cost_dev, best_cost_train))\n",
    "print('  err_dev: %f' % (err_dev_min))\n",
    "print('  nb_parameters: %d (%s)' % (nb_parameters, humansize(nb_parameters)))\n",
    "print('  time_per_it: %fs\\n' % (runtime_per_it))\n",
    "\n",
    "\n",
    "\n",
    "## Save results for plots\n",
    "# np.save('results/test_predictions.npy', test_predictions)\n",
    "np.save(results_dir + '/cost_train.npy', kl_cost_train)\n",
    "np.save(results_dir + '/cost_train.npy', pred_cost_train)\n",
    "np.save(results_dir + '/cost_dev.npy', cost_dev)\n",
    "np.save(results_dir + '/err_train.npy', err_train)\n",
    "np.save(results_dir + '/err_dev.npy', err_dev)\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# fig cost vs its\n",
    "\n",
    "textsize = 15\n",
    "marker=5\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "fig, ax1 = plt.subplots()\n",
    "ax1.plot(pred_cost_train, 'r--')\n",
    "ax1.plot(range(0, nb_epochs, nb_its_dev), cost_dev[::nb_its_dev], 'b-')\n",
    "ax1.set_ylabel('Cross Entropy')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "lgd = plt.legend(['test error', 'train error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})\n",
    "ax = plt.gca()\n",
    "plt.title('classification costs')\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "plt.savefig(results_dir + '/cost.png', bbox_extra_artists=(lgd,), bbox_inches='tight')\n",
    "\n",
    "plt.figure()\n",
    "fig, ax1 = plt.subplots()\n",
    "ax1.plot(kl_cost_train, 'r')\n",
    "ax1.set_ylabel('nats?')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "ax = plt.gca()\n",
    "plt.title('DKL (per sample)')\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "fig2, ax2 = plt.subplots()\n",
    "ax2.set_ylabel('% error')\n",
    "ax2.semilogy(range(0, nb_epochs, nb_its_dev), 100 * err_dev[::nb_its_dev], 'b-')\n",
    "ax2.semilogy(100 * err_train, 'r--')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "ax2.get_yaxis().set_minor_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "ax2.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "lgd = plt.legend(['test error', 'train error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})\n",
    "ax = plt.gca()\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "plt.savefig(results_dir + '/err.png',  bbox_extra_artists=(lgd,), box_inches='tight')\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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yDAXJCFJt7TpqS0RkGAqSESTb2siuW0emd12tSxERqUsKkhGk2icAkFmtkxJFRApRkIwgGbuUvIiIrE9BMgL1SEREhqcgGUGqLeqRrF5V40pEROqTgmQEut2uiMjwFCQjGLgCsMZIREQKUZCMINm/a0tBIiJSiIJkBIlkkmRrq663JSIyBAVJEVITJmiwXURkCAqSIqQmdJBeubLWZYiI1CUFSRFSHQoSEZGhKEiKkOqYSHqVgkREpBAFSRFyPZJstuq3jRcRqXsKkiKkOjrI9vWRWbu21qWIiNQdBUkRUhM6ADROIiJSgIKkCA0TJwKQXrmixpWIiNQfBUkRUh3qkYiIDEVBUoSBIFGPREQkn4KkCBojEREZWkM1N2ZmxwMfAPYBJgEOXObu1+S1+xhwNrAN8ChwtrvfXs1a45LNzSSamhQkIiIFVLtHcgawCvgU8A5gLnC1mZ2aa2Bm7wW+B/wvcCQhSG42s92qXOsgqY4O+nRSoojIeqraIwGOcffFsdd3mNlWhIC5Ipp2IfAzd/8SgJndCewFnAO8v5rFxqU6JpJeoTESEZF8Ve2R5IVIzkPA5gBmtj2wE/Cr2DIZ4DpC76RmGnS9LRGRguphsH1/4LHo+czo5/y8No8Dm5rZZlWrKk+qo0PX2xIRKSBRy+tHmdlhwK3AR9z9KjP7T+AXwCbuvjzW7vConbn7E0WufhqwYPbs2SxatKjsWt/ftS1v23wq7//738pel4hIverq6mLevHkA04GFxSxT7TGSfmY2DbgauNHdr8qbnZ9uiSGmj2jOnNvJZEoPy1mzZvLwwwMdo6W3zGHxr3/F3+97mGRLS8nrGy35ddWTeq1NdZWmXuuC+q1tPNWVTCZGbpS/TMlLjAIz2xS4BXiOwQPoy6KfnXmL5F4vp0Z0druISGFVDxIzawNuBpqAo909fjP0XHTOzFtsJrDU3V+rQokFpSaGIOlTkIiIDFLUri0z2wU4HFgKXOvuvRuyMTNrIByBtSNwgLu/Gp/v7s+Y2RPA8cAfo2WS0etbNmSboyU1QRduFBEpZMQgMbN3Er78M4RexJlmdrS7v7gB2/sOcBRwOuEorDfG5j3k7j3AF4BfmNlC4G7gvwjB8x8bsL1R06BdWyIiBRWza+tLwK+BDsIXeha418z22oDtvTX6eTlwb95jS4DocikfBz4E/AHYA3i7uz+yAdsbNRojEREprJhdW9sDp0e7s542s4OAG4EHzGw58Li7v9nMXgKS7r7FUCty92nFFOXuPwR+WEzbakn0X29Lu7ZEROKKCZLngK7cC3dfGZ3X8W7CWei5I6m+y8BhuuNOIpGgYVInfa/X7MAxEZG6VEyQ3AB82sx+4e5ZGHTZkn7u/sUK1FdXGjo76VuuIBERiSsmSL4K/A2YCLxe2XLqW2pSJz3PP1frMkRE6sqIQRJdquTGKtRS9xo6O1n9r3/WugwRkbpS0iVSzOxAYFN3vzF6PQX4FrALcDtwzoaeYzIWNHR2ku1ZS2ZtN8mW1lqXIyJSF0o9s/1SIH6DqcuBw4D7CIfrXjg6ZdWnhs5wpRaNk4iIDCg1SAx4EPovdXIs4dDgjxNujXvC6JZXXxo6NwEUJCIicaUGSROwNnp+AGHX2O+j108QnVQ4Xg30SJaN0FJEZONRapDMB94WPf9P4F53z53qvRXhWlzjVmpSFCSvb9QHr4mIDFLq/Ui+CFxnZh8FJgHvjM17G+G2ueNWsqWFRHOzdm2JiMSUFCTu/jsz2xnYC/hX3t0K7wX+MZrF1ZtEIkFDZydp7doSEelX6uG/HwR+7+6/LjD7euDtwLzRKKxeNUzS2e0iInGljpH8FNhhiHnTo/njWkPnJgoSEZGYUoNkuIsyTgbG/aVxGzrDhRuz2dLvAy8iMh4Ve2Or+KD6+WaWf8vbFuBA4P5RrK0uNXR2kl23jkz3GlJt7bUuR0Sk5ooZI9kc2D32egdgal6bdcCfgC+PUl11q/8Q4OXLFSQiIhR30cb+m0yZ2VzgJHefX+nC6lX8MinNW21d42pERGqv1MN/D6lUIWNFLkjSusGViAhQ+gmJmFkHYcxkJ8LYyCDufvYo1FW3+q+3tUznkoiIQOnnkewA3A20Ae3Aa8Cm0XqWEW58Na6DJNncTGpCB71LFte6FBGRulDq4b/fAB4AtiAcCnwU0Aq8H1jFOL/6b07D5Mn0LlaQiIhA6bu29gNOBHqi103ungaujm5ydTmw/yjWV5cap0yh54VFtS5DRKQulBokLcAKd8+Y2VLCFX9zHgH2HGkFZjYDOAt4I+EmWXe5+8F5bRYC2+Ut+oq75x92XBONk6ew+p//IJvNkkgMd46miMj4V2qQPMHAF/xDwMfNbA6QBj4KvFjEOnYl7BK7j3B/k6FcDVwRe72uxForpmHyZLK9vaRXrKBh0qRalyMiUlOlBskvgVnAz4HzgT8SLouSidb1oSLWcVPsnu/XA1OGaPeSu99XYn1V0Tg5lNy7ZImCREQ2eqWeR/L12PP7zGw3wn1IWoE73P2RItaRKbnKOpMLkr4li2H77WtcjYhIbZV8HgmAme0EdBHGTF6IJm9rZtu6+5xRqu0jZnYa0A3cCpzp7s+O0rrL0jB5MoCO3BIRARKlXMXWzHYBrgV2ofCVgLPuniphfdcDUwoMtl9OGENZBOwMfJ4wDrO7uxd7n9tpwILZs2ezaNHoH2H1k1lv4O6li/nxcwtHfd0iIrXS1dXFvHnzINwaZGExy5TaI/k+YYD8OOAxKjQA7u6nx17eZWb3AA8DHwa+Wcq65sy5nUym9Eu+z5o1k4cfHvqSYs9eeAHH7bU3p55+RsnrLsdIddVSvdamukpTr3VB/dY2nupKJks/ErXUINkLeK+731zylsrg7o+YmQN7V3O7w2mYMoXeV16pdRkiIjVX6pntT1Pg+lpVVDd3k2qcPJneJYt1gysR2eiVGiRnAp81s6oeqhQdHWbAg9Xc7nAaJ08h29NDZtWqWpciIlJTpe7auhjYGpgfnX2+3rXU3X2/4VZgZm2EExKJ1jXRzN4TvZ4DHEK4dtfNhBMcZwKfA54Driqx3oppnDJwLkmqo6PG1YiI1E6pQfJI9CjH5sB1edNyr6cDz0dtvgl0AkuAPwCfdfe6uSd8Q+6kxMWv0TJtWm2LERGpoVJPSPxwuRt094UUPnQ47rByt1NpjZttDkDvqxpwF5GNW6ljJBJJtbaSmjSJdTpyS0Q2cgqSMjRtMZV1L79U6zJERGpKQVKGpqlTdS6JiGz0FCRlaNxiKulVK0nrEGAR2YgpSMrQtEW4z9a6V16ucSUiIrWjIClD09QtAVj3soJERDZeCpIyNE6ZAqkUveqRiMhGTEFShkRDA41TNtOuLRHZqClIytQ0dap2bYnIRk1BUqamLabS+8rLZDNj/g7CIiIbREFSpsapU8n29dG3dEmtSxERqQkFSZkGjtzSGe4isnFSkJSpeesuAHqeH/37wouIjAUKkjKl2ttp2HRTehY9V+tSRERqQkEyCpq7tqHneQWJiGycFCSjoHmbbVn38stketfVuhQRkapTkIyC5m22gUyGdS+8WOtSRESqTkEyCpq7tgXQOImIbJQUJKOgcfPNSTQ10fP887UuRUSk6hQkoyCRTNLc1aUBdxHZKClIRklz17b0LHqebDZb61JERKpKQTJKmrfZhsyaNbpUiohsdBqqvUEzmwGcBbwR2A24y90PzmuTAM4FTgKmAPcDp7n7w9WttnjN200HYO2CBTROnlLjakREqqcWPZJdgaOAJ6JHIecA5wNfAY4BVgG3mdnUqlS4AVq23ZZEYyPdTz9V61JERKqqFkFyk7tv4+7HA4/mzzSzFkKQXOzuV7r7bcDxQBY4pbqlFi/R0EDLtOmsVZCIyEam6kHi7iPduGN/YCLwq9gyq4GbgCMrWFrZWrbfgbXPLtQZ7iKyUanHwfaZQBp4Mm/649G8utU6Ywak0/Q8+2ytSxERqZpELQ9XNbPrgSnxwXYzOw84y90789qeCPwQaHb3Yv7knwYsmD17NosWVecS75MaGvnhrH34+fPPctMruj+JiIw9XV1dzJs3D2A6sLCYZap+1FaRCqVbYph5Q5oz53YymdLDctasmTz88PySl1twzlmctPc+fOnkU0tethgbWlc11Gttqqs09VoX1G9t46muZDIxcqP8ZUpeovKWAR1mlsqb3gmscffeGtRUtJYdZtD99FM6MVFENhr1GCTzgRQwI2/6zGheXWudMYP066/T+9prtS5FRKQq6jFI7gFWEA75BcDM2gjnk9xSq6KK1bbzLgCseeyRGlciIlIdtTizvY1wQiLA1sBEM3tP9HqOu68xs0uA881sGaEXcgYh9K6odr2latxiKg2TJ7Pm0UfpPPjQWpcjIlJxtRhs3xy4Lm9a7nXuKIFLCMFxLjAZeAA4wt1fqVKNGyyRSNC+626svP9vZPv6SDTU6/EMIiKjo+rfcu6+kIEjsIZqkwUuih5jTtsuu/H6X+5k7YIFtO64Y63LERGpqHocIxnz2nbeBRIJVmucREQ2AgqSCki1t9MyfXvWPKogEZHxT0FSIW277sbaBc/Qt2JFrUsREakoBUmFdOz9BshmWfXQg7UuRUSkohQkFdLU1UXj1KmsvP9vtS5FRKSiFCQVkkgk6HjDvnT7fPpef73W5YiIVIyCpII63rBf2L31d+3eEpHxS0FSQU1bd9E0dUtW3j+v1qWIiFSMgqSCEokEHfvNpvvJJ1j32qu1LkdEpCIUJBU28c1vAeD1O/9c20JERCpEQVJhjZtuSvues1hx911keuv6VioiIhtEQVIFnQcfSnrlSg26i8i4pCCpgrZddqVxs81YPvf2WpciIjLqFCRVkEgm6Tzsrax96knWzH+81uWIiIwqBUmVTHrLQaQmdbLkphtrXYqIyKhSkFRJsqmJTY88im6fr16JiIwrCpIqmvSWg0lNmsTiG35NNputdTkiIqNCQVJFyaYmprzrONY+/RQr7rm71uWIiIwKBUmVTTzgQFp2mMHi668lvXp1rcsRESmbgqTKEskkm//nB0ivWsVr119b63JERMqmIKmBlm23Y5N/O5IVd/2FlQ/ofiUiMrYpSGpkyruOo2X77XnlZz/VBR1FZEyryyAxsw+ZWbbA4+O1rm20JBoa2PK/T4JEghevuFzjJSIyZtVlkMQcCrwp9vhNbcsZXY1TNmOrk06h99VXePHKy8msW1frkkREStZQ6wJGcL+7r6p1EZXUtvMuTP3of/PSD77LC9/6BlufchrJltZalyUiUrR675FsFDr23Y+pHzmR7iec57/6Fd3jXUTGlHoPkqfNrM/M3Mz+p9bFVNLENx3AVqecxrqXXuTZL16gy6iIyJhRr0HyEnA+8AHgGGAe8D0z+1RNq6qwCXvMYtvPnk+ytZVFX7uUV6+9hszatbUuS0RkWImxcs0nM7sWOBzYzN0zRSwyDVgwe/ZsFi1aVNHaRltzMskHu7bjiM23YPG6Hn71wiL+suQ1ivmlRUTK0dXVxbx58wCmAwuLWabeB9vjrgf+nRAQzxS70Jw5t5PJlB6Ws2bN5OGH55e83Gjqfvopmq/5P05uaub02fvTefgRHPXp07n374/WtK6h1MN7VojqKk291gX1W9t4qiuZTJS8nXrdtTWcsdGFGgWtO8xg2/MuYKtPnEaqvZ3Xrv4F399zb1787pWs+Nt9ZNZ217pEEZEx1SN5N7AYeLbWhVRTIpFgwl57M2GvvVm74Bl+ePrJHPXUk6x68AESDQ20ztyZ1hk70jpjR1qmb0+yubnWJYvIRqYug8TMfg38DfgnkAJOiB6nFTk+Mi61TN+eHz+3kFN+O4e1Tz/FygfvZ81jj7Hkt9F5mskkTVO3pGnLLWnaciuattyKxs02o3HTTUlNnEQiORY7oCJS7+oySAAHPgJsAySAx4APuvvPa1pVnUgkk7TuuBOtO+4EQHr1atYueJrup56k5/nn6Xn+eVb9/UGIH0iRStHQ2UnjppNp2GRTUhMmkJowgWR7O6n29vC8bUJ43t5OormZREMDiUTp+0tFZONSl0Hi7p8FPlvrOsaKVHs77bvtQftue/RPy/Suo/fVV+ldvJi+pUu6r5DMAAAP70lEQVTpW7aU3qVL6Fu6lLULniG9ehWZNWuGX3EySbK5mWRLC4mm5sHPW5pJNreQbG4i0dxCsrmZt2+xJcvn3k6isTE8GhpJNjUNvG5sJBl7nmiM5imwRMa0ugwSKV+ysYnmrbto3rpryDbZdJrMmjWkV68mvXpVCJfVq0mvXkN2XQ+ZtWvJ9PSQ6ekh2zPwPL1qJX2LX+t/nelZC+k0H9xmO179vw3rNMaDZXDYxAKoqYlEQ960hob+0Eo0Ngx+3dBAorGB3Tsm0v3kE+H1kO0atetPZAMpSDZiiVSKVEcHqY6OsteV7etj//325K4/30u2dx3Z3l6yvb1kenvJrltHti/+urd/fravl8y6dUO/7u0j07uOvu7u2Drj83sH78Ir4Hzbhee/8v9G/iVSqShsBgIn2R88jYODqDH+unFgueh5srFQ+8HtprW20fPiC4NCLxm1I5VSL03GDAWJjIpEQwPdmTQNEydWfdvZdJpsX18URH39gZTt6yPT28eHP3gCP/7+T8O8vlybwe36l+vtJZPfrq+3f92ZdT1kV6+KpvXlrTP8LNalu+7BsxecN+T89XtbjYPCKh5gyeF6ZYXCL2qTjIdg9NisqZm+5ctIpBr6t6tgk+EoSGTMS6RSJFIpGOLQ5/mrVtK+625VqSWbzQ4EU99Ar2rQ6+j56af8D9/46uVRePUWCMO+wYEVD74o4NJr1w4bkGRKP8jx23vsxTOfXv9qRPHeGg2p0FtraIBYmIVeXa43NzAv0dAQgqmhwCMXWP3zU/1h17++htBb3KK5md6lS9ZbhmRSQVdDChKRUZRIJEg0NkJjIzD87QAeWL6Mjn33q2g92UwmtttwcOBkBvXIosBK93H+uZ/mwgu+PLi3lguydDoWbr1k+9LrtcusXTt4e1GPkVibDXXF7nux4Owz15+RSBQMJmKhlBi067KIgOsPsoZBYVZome3b2ul5/vnYduNBmgrtx/EYnIJEZBxLJJMkmpuH7K0VcueSxXQedHDFaspms5BOh4Dp7SWb7osCKQqadN/gAMuFT18fnz3nTL584UWx6VGQpWPP+9cVPU8PDrFM95r+0BwchOn+bZNOl/Q7XbLL7jx74fnDN4p6zoNCab3eWC7A4tPzwiw+Lwo6GuKvUzROntJ/ekA1KEhEpKoSiUT/Lq9SAg7grqWLmXTgQRWqbEA2kxnck0r3xcInL4DSaU47+US++fUrBodZOvTiSKfXD8b11tfXv65Mby/Z7u7BvbgC4TrsQSapFDMu/3bF36ccBYmISJ5EMhl2RTU2FtX+wdeX07HPvhWuarBsJjMQKun0oJBLtraSbGmpWi0KEhGRMSiRTJJoaoKmplqXMiav/isiInVEQSIiImVRkIiISFkUJCIiUhYFiYiIlEVBIiIiZRnPh/+mYMNuZA/Q1dW1wctWUr3WBfVbm+oqTb3WBfVb23iqK9Y+VewyiewIl+Aew94M3FXrIkRExqgDgb8W03A8B0kzsC/wElDahXNERDZeKWBL4H6gp5gFxnOQiIhIFWiwXUREyqIgERGRsihIRESkLAoSEREpi4JERETKoiAREZGyKEhERKQs4/kSKSUzs12AK4A3AcuBHwEXunvVTmg0s+OBDwD7AJMABy5z92vy2n0MOBvYBngUONvdb69inVtHtbUDHe6+KpqeAM4FTgKmEE5qOs3dH65gLQ3Ap4GPAtsCrwHXufunYm2qXle03fcS/p12Al4HbgfOcfcXq1Wbmc0AzgLeCOwG3OXuB+e1KaqG0fyMjFSXmW0JnAG8FdgBWAbcAZwbf/+itlsDVwJHAGuBXxI+E2tGu64C7b8JnA58zd0/nTdvVL9Tiq3NzHYHLiacnZ4EHgdOcvcHK1GbeiQRM9sEuA3IAu8EvgicCVxY5VLOAFYBnwLeAcwFrjazU2O1vhf4HvC/wJGEILnZzHarYp1fjerMdw5wPvAV4JiozW1mNrWCtfwUOA24jPClcw7QXeu6zOwdwDXAPYT/U58B3kL4t4p/9ipd267AUcAT0aOQEWuowGdkpLr2AY4lvIfHEL5AZwP3mNmEWF0NwB+B7YATCF/qxwM/qFBd/aIv448AKwrMq8R3yoi1mdkswv+55YT343jgJqC1UrWpRzLg44Q3+jh3XwHcamYTgS+Y2aXRtGo4xt0Xx17fYWZbEQLmimjahcDP3P1LAGZ2J7AX4cvg/ZUu0MwOBN4G/D9CoOSmt0Q1XOzuV0bT7gUWAqcAn6tALW8D3gvs6e6PDdGm6nVF/gP4u7ufEqtlBXAjYMDjVartJne/MVr39YQeR78Sahjtz8iwdRGu8zTT3ftitf6d0BN+N/CzaPLxwM7ADHdfELXrBX5pZhe6+5OjXFfct4DLCXsR8lXiO6WY2r4XtYt/F/yhkrWpRzLgSOCPeW/gLwlv9kHVKiIvRHIeAjYHMLPtCbtJfhVbJgNcR/gdKsrMUoRA+yKQX+v+wMS82lYT/hqqVG0fAe4YKkRqWBdAI2F3Vtzy6GfuEqsVry36/zGcYmsY1c/ISHW5+/J4iETTngDWEH0eYnXdnwuRyG+BdYQ/eEa1rhwzew8hwC4Zosmof6eMVFvUQ5rNwB+dQxnV2hQkA2YC8+MT3P05wn/amTWpaMD+QO6LMlfL/Lw2jwObmtlmFa7l40AL8O0C82YSLpCZ/xfg41TuPZwNPGFmV5rZCjNbY2a/iXpxtawL4CfAgWb2QTObaGY7AV8G5saCr1a1xRVbQ80/I2a2B9DGwOdhqLrWAU9Xqi4zawW+RhjvWj1Es1q8X7Ojn5uY2T/MrM/Mnjazj1ayNgXJgE0Y+Gsxblk0rybM7DDCPszcF3eulvxal+XNr0Qtk4EvAWe4e2+BJpsAqwoM1i0D2sysqQJlTQU+BMwi7OL6MGHf+g3RAHKt6sLdfx/V9gNCz8QJV1Y9LtasJrXlKbaGmn5GonGlywmB96fYrFrUdS7hyuK/GKZNLerKjWn9L/B/hIMP/gD8yMyOqlRtGiMZrNClkBNDTK84M5sGXA3c6O5X5c3OrykxxPTRdBEwz93nDNNmqPdwqHnlSkSPd7r7EgAzewm4EziUcJRULerCzA4h7K++HLgF2AL4AiHkDo99cVe9tgKKraGWn5GLCUcYHVTgD5mq1WVm0wlHCR7q7iOtv9rvV65z8CN3vzR6PtfMdiaEX/yzO2q1qUcyYBnQWWD6JAond0WZ2aaEL5/nGDyAnut55Neae12RWs1sV8J4xIVm1mlmnYRdDACToq7+MqAjGkfJr23NEL2Yci0D/pULkchfCfvHd4m1qXZdEHZ9/M7dP+Puf3b3a4F3AQcTepm1rC2u2Bpq9hkxs5MJR239l7vPy5s9VF2dFarrEsJnc37ss5AEmqPXuQCuxfu1NPo5N2/6HQx8HmCUa1OQDJhP3r5BM9uGcJ5E/nhERZlZG3Az0AQcnbcPNldL/n7MmcBSd3+tQmXtSBg8vpfwn3AZA7vbFhEG9+YTdt3MKFBbpd7Dx4eYngByA5O1qCu3/kHnYbi7Ew5N3qHGtcUVW0NNPiNm9m7C/6+zozDOV6iuJmD7CtVlhN2Ty2KPbQhHuC0Dth6mrkq/X8V8HmCUa1OQDLgF+Dcz64hNO4Hwob+zWkVEx8RfR/jiPtLdX43Pd/dnCMePHx9bJhm9vqWCpf0VOCTv8ZVo3lGEw4DvIRxPH6+tjXAOQKVquxnYw8zih0G+hRB6/4he16IugGeBveMTol0MrYRDa2tZW1yxNVT9M2JmBxP29V/p7pcN0ewWYF8z2y427R2Eu6TmH/Y6Gk5k/c/CK4Sj3g4hnBCbq6va3yn3EMLssLzphzHweRj12nSHxEh0gs5jwCOEL8jtga8D33T3Sp1nUKiOHwAfI5xU9be82Q+5e4+ZvY8wyPd54G7gvwj/CfZ190eqWOuHCCcDxs9sP5dwYttZhL9sziAcSbKru79SgRomEv7NXiCc19JB+Peb7+5HxNpVta5om6cD34geuTGSCwg9zd1yPc1K1xaFQm6g9UzCob6fj17Pcfc1xdQw2p+RkeoinGCYO5/lZAb/Rf2auz8draeRcIh8T/Q7TCK857flnUsxKnUVOlvezBYC18fPbK/Ed0qR/5afBC4ljMfdTzjn5r8JY0t3VaI2DbZH3H1ZdITUlYTj55cT/jN+ocqlvDX6eXmBedOBhe5+jYUzez9D+OA8Cry9miEyjEsIPd1zgcnAA8ARlfqydvcVZnYo4cSwXxLGRm4kXBmgZnVFvhXVcxLhsOnlhJ7duXm7Kytd2+aEXm5c7vV0whf1iDVU4DMyUl2zCaGwJ+EPprifEY6Iw917oxNTryT0CnoI/xfOqlBdC4tZSYW+U0aszd2/Ge2lODXalgPvyYVIJWpTj0RERMqiMRIRESmLgkRERMqiIBERkbIoSEREpCwKEhERKYuCREREyqLzSETGkOhM77nA7nVy3pCIeiQiIlIeBYmIiJRFu7ZEimBmbybcj2VfwoXtfkO4wdfK2DXH9iNcr+gNwPPAZ9z9hrz1nEK4jtq2UZtvu/s38trsEW3rQMJn9DHgPHe/NdZsipnlbq/8KnCZu39nVH9pkSKpRyIyAjM7gHCDrJeB9wCfJFw476d5Ta8lXOfrOOBfwHVmtmdsPR8jXA79d4Qr614HfM3Mzom1mUm4rtSWhOtzHQvcQLhMedwPCVdzPRb4M/BtM9uv/N9WpHTqkYiM7BLgHnc/ITfBzF4Abjez3WLtfpS71LmZ/ZHQkzgXeG90Eb0vAFe5+5lR+z+Z2STgXDP7pruvJVzJ9XXgQHfvjtrFeyI517j7l6Nt/ZkQTMex/hWjRSpOPRKRYUSX7X4T8Csza8g9CFfx7SXcHz6nfzeWu2cIvZNcL6EL2Ir1r9x6LeFS4LtHrw8Fro2FyFD671ke3cHwyWgbIlWnIBEZ3iaEuwd+hxAcuUcP4eZZ8V1Or+Yt+yphFxWxn/mXhs+93jT6ORl4qYi68m+Hug5oKWI5kVGnXVsiw1sOZAm7peYUmP8iA/eQ2RyI3zt+cwZC4aXYtLgtop+5e20vYSB0RMYEBYnIMNx9tZndB5i7f7FQGzPLPT2W6J7Z0ZjIOxkYs1hECJ38WyL/O+E2t/+KXt8O/LuZnReNmYjUPQWJyMjOJgysZ4DrgZWEw3ePBs6LtTvRzNYRbl/6MWAG8D4IYyZm9gXg+2a2hDCAfhDh7omfjYXGhYTbo/7FzL5G6KHsBSxx959U9LcU2UAaIxEZgbv/FXgLsBnwc8KtSc8mnAcSH/N4L6FX8lvC7WFPcPeHYuv5IXBa1OZmQsic6e6XxNo48GZgMfAjwgD+e4BnK/TriZRNt9oVKVPshMQOd19V43JEqk49EhERKYuCREREyqJdWyIiUhb1SEREpCwKEhERKYuCREREyqIgERGRsihIRESkLAoSEREpy/8H4kJp3tARFo8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "textsize = 15\n",
    "marker=5\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "fig, ax1 = plt.subplots()\n",
    "ax1.plot(pred_cost_train, 'r--')\n",
    "ax1.plot(range(0, nb_epochs, nb_its_dev), cost_dev[::nb_its_dev], 'b-')\n",
    "ax1.set_ylabel('Cross Entropy')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "lgd = plt.legend(['test error', 'train error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})\n",
    "ax = plt.gca()\n",
    "plt.title('classification costs')\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "plt.savefig(results_dir + '/cost.png', bbox_extra_artists=(lgd,), bbox_inches='tight')\n",
    "\n",
    "plt.figure()\n",
    "fig, ax1 = plt.subplots()\n",
    "ax1.plot(kl_cost_train, 'r')\n",
    "ax1.set_ylabel('nats?')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "ax = plt.gca()\n",
    "plt.title('DKL (per sample)')\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "fig2, ax2 = plt.subplots()\n",
    "ax2.set_ylabel('% error')\n",
    "ax2.semilogy(range(0, nb_epochs, nb_its_dev), 100 * err_dev[::nb_its_dev], 'b-')\n",
    "ax2.semilogy(100 * err_train, 'r--')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "ax2.get_yaxis().set_minor_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "ax2.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "lgd = plt.legend(['test error', 'train error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})\n",
    "ax = plt.gca()\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "plt.savefig(results_dir + '/err.png',  bbox_extra_artists=(lgd,), box_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[36m\n",
      "Data:\u001b[0m\n",
      "\u001b[36m\n",
      "Network:\u001b[0m\n",
      "\u001b[36m\n",
      "Net:\u001b[0m\n",
      "\u001b[33m Creating Net!! \u001b[0m\n",
      "    Total params: 4.79M\n",
      "\u001b[36mReading models_weight_uncertainty_MC_MNIST_gaussian/theta_last.dat\n",
      "\u001b[0m\n",
      "  restoring epoch: 159, lr: 0.001000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:7: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.from_iter(generator)) or the python sum builtin instead.\n",
      "  import sys\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "159"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "from __future__ import division\n",
    "import time\n",
    "import torch.utils.data\n",
    "from torchvision import transforms, datasets\n",
    "import matplotlib\n",
    "import copy\n",
    "\n",
    "\n",
    "models_dir = 'models_weight_uncertainty_MC_MNIST_gaussian'\n",
    "results_dir = 'results_weight_uncertainty_MC_MNIST_gaussian'\n",
    "\n",
    "mkdir(models_dir)\n",
    "mkdir(results_dir)\n",
    "# ------------------------------------------------------------------------------------------------------\n",
    "# train config\n",
    "NTrainPointsMNIST = 60000\n",
    "batch_size = 100\n",
    "nb_epochs = 160\n",
    "log_interval = 1\n",
    "\n",
    "savemodel_its = [20, 50, 80, 120]\n",
    "save_dicts = []\n",
    "\n",
    "# ------------------------------------------------------------------------------------------------------\n",
    "# dataset\n",
    "cprint('c', '\\nData:')\n",
    "\n",
    "\n",
    "# load data\n",
    "\n",
    "# data augmentation\n",
    "transform_train = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=(0.1307,), std=(0.3081,))\n",
    "])\n",
    "\n",
    "transform_test = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=(0.1307,), std=(0.3081,))\n",
    "])\n",
    "\n",
    "use_cuda = torch.cuda.is_available()\n",
    "\n",
    "trainset = datasets.MNIST(root='../data', train=True, download=True, transform=transform_train)\n",
    "valset = datasets.MNIST(root='../data', train=False, download=True, transform=transform_test)\n",
    "\n",
    "if use_cuda:\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=True, num_workers=3)\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=3)\n",
    "\n",
    "else:\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=False,\n",
    "                                              num_workers=3)\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=False,\n",
    "                                            num_workers=3)\n",
    "\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# net dims\n",
    "cprint('c', '\\nNetwork:')\n",
    "\n",
    "lr = 1e-3\n",
    "nsamples = 3\n",
    "########################################################################################\n",
    "net = Bayes_Net(lr=lr, channels_in=1, side_in=28, cuda=use_cuda, classes=10, batch_size=batch_size,\n",
    "          Nbatches=(NTrainPointsMNIST/batch_size))\n",
    "\n",
    "\n",
    "net.load('models_weight_uncertainty_MC_MNIST_gaussian/theta_last.dat') # theta_last.dat\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## inference with sampling on test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 200\n",
    "\n",
    "if use_cuda:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=4)\n",
    "\n",
    "else:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=False,\n",
    "                                            num_workers=4)\n",
    "test_cost = 0  # Note that these are per sample\n",
    "test_err = 0\n",
    "nb_samples = 0\n",
    "test_predictions = np.zeros((10000, 10))\n",
    "\n",
    "Nsamples = 100\n",
    "\n",
    "net.set_mode_train(False)\n",
    "\n",
    "for j, (x, y) in enumerate(valloader):\n",
    "    cost, err, probs = net.sample_eval(x, y, Nsamples, logits=False) # , logits=True\n",
    "\n",
    "    test_cost += cost\n",
    "    test_err += err.cpu().numpy()\n",
    "    test_predictions[nb_samples:nb_samples+len(x), :] = probs.numpy()\n",
    "    nb_samples += len(x)\n",
    "\n",
    "# test_cost /= nb_samples\n",
    "test_err /= nb_samples\n",
    "cprint('b', '    Loglike = %5.6f, err = %1.6f\\n' % (-test_cost, test_err))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## rotations, Bayesian"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First load data into numpy format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 1, 28, 28)\n",
      "(10000,)\n"
     ]
    }
   ],
   "source": [
    "x_dev = []\n",
    "y_dev = []\n",
    "for x, y in valloader:\n",
    "    x_dev.append(x.cpu().numpy())\n",
    "    y_dev.append(y.cpu().numpy())\n",
    "\n",
    "x_dev = np.concatenate(x_dev)\n",
    "y_dev = np.concatenate(y_dev)\n",
    "print(x_dev.shape)\n",
    "print(y_dev.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot for single digit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 1, 28, 28)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:84: MatplotlibDeprecationWarning: The set_color_cycle function was deprecated in version 1.5. Use `.set_prop_cycle` instead.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x640 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## ROTATIONS marginloss percentile distance\n",
    "import matplotlib\n",
    "from torch.autograd import Variable\n",
    "\n",
    "def softmax(x):\n",
    "    \"\"\"Compute softmax values for each sets of scores in x.\"\"\"\n",
    "    e_x = np.exp(x - np.max(x))\n",
    "    return e_x / e_x.sum()\n",
    "        \n",
    "###########################################\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.ndimage as ndim\n",
    "import matplotlib.colors as mcolors\n",
    "conv = mcolors.ColorConverter().to_rgb\n",
    "\n",
    "#############\n",
    "im_ind = 90\n",
    "Nsamples = 90\n",
    "#############\n",
    "\n",
    "angle = 0\n",
    "plt.figure()\n",
    "plt.imshow( ndim.interpolation.rotate(x_dev[im_ind,0,:,:], 0, reshape=False))\n",
    "plt.title('original image')\n",
    "# plt.savefig('original_digit.png')\n",
    "\n",
    "\n",
    "s_rot = 0\n",
    "end_rot = 179\n",
    "steps = 10\n",
    "rotations = (np.linspace(s_rot, end_rot, steps)).astype(int)            \n",
    "  \n",
    "ims = []\n",
    "predictions = []\n",
    "# percentile_dist_confidence = []\n",
    "x, y = x_dev[im_ind], y_dev[im_ind]\n",
    "\n",
    "fig = plt.figure(figsize=(steps, 8), dpi=80)\n",
    "\n",
    "# DO ROTATIONS ON OUR IMAGE\n",
    "\n",
    "for i in range(len(rotations)):\n",
    "    \n",
    "    angle = rotations[i]\n",
    "    x_rot = np.expand_dims(ndim.interpolation.rotate(x[0, :, :], angle, reshape=False, cval=-0.42421296), 0)\n",
    "    \n",
    "    \n",
    "    ax = fig.add_subplot(3, (steps-1), 2*(steps-1)+i)\n",
    "    ax.imshow(x_rot[0,:,:])\n",
    "    ax.axis('off')\n",
    "    ax.set_xticklabels([])\n",
    "    ax.set_yticklabels([])\n",
    "    ims.append(x_rot[:,:,:])\n",
    "    \n",
    "ims = np.concatenate(ims)\n",
    "net.set_mode_train(False)\n",
    "y = np.ones(ims.shape[0])*y\n",
    "ims = np.expand_dims(ims, axis=1)\n",
    "\n",
    "cost, err, probs = net.sample_eval(torch.from_numpy(ims), torch.from_numpy(y), Nsamples=Nsamples, logits=False) # , logits=True\n",
    "\n",
    "predictions = probs.numpy()\n",
    "    \n",
    "    \n",
    "    \n",
    "textsize = 20\n",
    "lw = 5\n",
    "    \n",
    "print(ims.shape)\n",
    "ims = ims[:,0,:,:]\n",
    "# predictions = np.concatenate(predictions)\n",
    "#print(percentile_dist_confidence)\n",
    "\n",
    "c = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd',\n",
    "     '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']  \n",
    "                                         \n",
    "\n",
    "# c = ['#ff0000', '#ffff00', '#00ff00', '#00ffff', '#0000ff',\n",
    "#      '#ff00ff', '#990000', '#999900', '#009900', '#009999']\n",
    "\n",
    "ax0 = plt.subplot2grid((3, steps-1), (0, 0), rowspan=2, colspan=steps-1)\n",
    "#ax0 = fig.add_subplot(2, 1, 1)\n",
    "plt.gca().set_color_cycle(c)\n",
    "ax0.plot(rotations, predictions, linewidth=lw)\n",
    "\n",
    "\n",
    "##########################\n",
    "# Dots at max\n",
    "\n",
    "for i in range(predictions.shape[1]):\n",
    "  \n",
    "    selections = (predictions[:,i] == predictions.max(axis=1))\n",
    "    for n in range(len(selections)):\n",
    "        if selections[n]:\n",
    "            ax0.plot(rotations[n], predictions[n, i], 'o', c=c[i], markersize=15.0)\n",
    "##########################  \n",
    "\n",
    "lgd = ax0.legend(['prob 0', 'prob 1', 'prob 2',\n",
    "            'prob 3', 'prob 4', 'prob 5',\n",
    "            'prob 6', 'prob 7', 'prob 8',\n",
    "            'prob 9'], loc='upper right', prop={'size': textsize, 'weight': 'normal'}, bbox_to_anchor=(1.4,1))\n",
    "plt.xlabel('rotation angle')\n",
    "# plt.ylabel('probability')\n",
    "plt.title('True class: %d, Nsamples %d' % (y[0], Nsamples))\n",
    "# ax0.axis('tight')\n",
    "plt.tight_layout()\n",
    "plt.autoscale(enable=True, axis='x', tight=True)\n",
    "plt.subplots_adjust(wspace=0, hspace=0)\n",
    "\n",
    "for item in ([ax0.title, ax0.xaxis.label, ax0.yaxis.label] +\n",
    "             ax0.get_xticklabels() + ax0.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "\n",
    "# plt.savefig('percentile_label_probabilities.png', bbox_extra_artists=(lgd,), bbox_inches='tight')\n",
    "\n",
    "# files.download('percentile_label_probabilities.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# probs = sample_probs.mean(axis=0)\n",
    "# print(sample_probs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### All dataset with entropy\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "## ROTATIONS marginloss percentile distance\n",
    "import matplotlib\n",
    "from torch.autograd import Variable\n",
    "\n",
    "def softmax(x):\n",
    "    \"\"\"Compute softmax values for each sets of scores in x.\"\"\"\n",
    "    e_x = np.exp(x - np.max(x))\n",
    "    return e_x / e_x.sum()\n",
    "    \n",
    "        \n",
    "###########################################\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.ndimage as ndim\n",
    "import matplotlib.colors as mcolors\n",
    "conv = mcolors.ColorConverter().to_rgb\n",
    "\n",
    "Nsamples = 100\n",
    "\n",
    "s_rot = 0\n",
    "end_rot = 179\n",
    "steps = 16\n",
    "rotations = (np.linspace(s_rot, end_rot, steps)).astype(int)            \n",
    "  \n",
    "\n",
    "all_preds = np.zeros((len(x_dev), steps, 10))\n",
    "all_sample_preds = np.zeros((len(x_dev), Nsamples, steps, 10))\n",
    "\n",
    "# DO ROTATIONS ON OUR IMAGE\n",
    "for im_ind in range(len(x_dev)):\n",
    "    x, y = x_dev[im_ind], y_dev[im_ind]\n",
    "    print(im_ind)\n",
    "    \n",
    "    ims = []\n",
    "    predictions = []\n",
    "    for i in range(len(rotations)):\n",
    "\n",
    "        angle = rotations[i]\n",
    "        x_rot = np.expand_dims(ndim.interpolation.rotate(x[0, :, :], angle, reshape=False, cval=-0.42421296), 0)\n",
    "        ims.append(x_rot[:,:,:])\n",
    "    \n",
    "    ims = np.concatenate(ims)\n",
    "    net.set_mode_train(False)\n",
    "    y = np.ones(ims.shape[0])*y\n",
    "    ims = np.expand_dims(ims, axis=1)\n",
    "    \n",
    "#     cost, err, probs = net.sample_eval(torch.from_numpy(ims), torch.from_numpy(y), Nsamples=Nsamples, logits=False)\n",
    "    sample_probs = net.all_sample_eval(torch.from_numpy(ims), torch.from_numpy(y), Nsamples=Nsamples)\n",
    "    probs = sample_probs.mean(dim=0)\n",
    "    \n",
    "    all_sample_preds[im_ind, :, :, :] = sample_probs.cpu().numpy()\n",
    "    predictions = probs.cpu().numpy()\n",
    "    all_preds[im_ind, :, :] = predictions\n",
    "   \n",
    "    \n",
    "all_preds_entropy = -(all_preds * np.log(all_preds)).sum(axis=2)\n",
    "mean_angle_entropy = all_preds_entropy.mean(axis=0)\n",
    "std_angle_entropy = all_preds_entropy.std(axis=0)\n",
    "\n",
    "correct_preds = np.zeros((len(x_dev), steps))\n",
    "for i in range(len(x_dev)):\n",
    "    correct_preds[i,:] = all_preds[i,:,y_dev[i]]\n",
    "    \n",
    "correct_mean = correct_preds.mean(axis=0)\n",
    "correct_std = correct_preds.std(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n"
     ]
    }
   ],
   "source": [
    "print('a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(results_dir+'/correct_preds.npy', correct_preds)\n",
    "np.save(results_dir+'/all_preds.npy', all_preds)\n",
    "np.save(results_dir+'/all_sample_preds.npy', all_sample_preds) #all_sample_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def errorfill(x, y, yerr, color=None, alpha_fill=0.3, ax=None):\n",
    "    ax = ax if ax is not None else plt.gca()\n",
    "    if color is None:\n",
    "        color = ax._get_lines.color_cycle.next()\n",
    "    if np.isscalar(yerr) or len(yerr) == len(y):\n",
    "        ymin = y - yerr\n",
    "        ymax = y + yerr\n",
    "    elif len(yerr) == 2:\n",
    "        ymin, ymax = yerr\n",
    "    ax.plot(x, y, color=color)\n",
    "    ax.fill_between(x, ymax, ymin, color=color, alpha=alpha_fill)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=100)\n",
    "\n",
    "errorfill(rotations, correct_mean, yerr=correct_std, color=c[2])\n",
    "          \n",
    "    \n",
    "plt.xlabel('rotation angle')\n",
    "lgd = plt.legend(['correct class', 'posterior predictive entropy'], loc='upper right',\n",
    "                 prop={'size': 15, 'weight': 'normal'}, bbox_to_anchor=(1.4,1))\n",
    "ax = plt.gca()\n",
    "ax2 = ax.twinx()\n",
    "errorfill(rotations, mean_angle_entropy, yerr=std_angle_entropy, color=c[3], ax=ax2)\n",
    "\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] + [ax2.title, ax2.xaxis.label, ax2.yaxis.label] +\n",
    "            ax.get_xticklabels() + ax.get_yticklabels() + ax2.get_xticklabels() + ax2.get_yticklabels()):\n",
    "    item.set_fontsize(15)\n",
    "    item.set_weight('normal')\n",
    "plt.autoscale(enable=True, axis='x', tight=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Weight distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(23928000,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Total parameters: 2392800, samples: 10')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "name = 'Bayes_By_Backprop'\n",
    "Nsamples = 10\n",
    "weight_vector = net.get_weight_samples(Nsamples=Nsamples)\n",
    "np.save(results_dir+'/weight_samples_'+name+'.npy', weight_vector)\n",
    "\n",
    "print(weight_vector.shape)\n",
    "\n",
    "fig = plt.figure(dpi=100)\n",
    "ax = fig.add_subplot(111)\n",
    "symlim = 0.7\n",
    "\n",
    "lim_idxs = np.where(np.logical_and(weight_vector>=symlim, weight_vector<=symlim))\n",
    "\n",
    "sns.distplot(weight_vector, 500, norm_hist=False, label=name, ax=ax)\n",
    "ax.set_xlim((-symlim, symlim))\n",
    "# ax.hist(weight_vector, bins=70, density=True);\n",
    "\n",
    "ax.set_ylabel('Density')\n",
    "ax.legend()\n",
    "plt.title('Total parameters: %d, samples: %d' % (len(weight_vector)/Nsamples, Nsamples))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evolution over iterations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DEBEEOwCxMDAyrUJpMeH+GmbEurzhkCVPAEQFwQ5ALBwcmi7f9rMrVpIOeI4NAGFGsAMQC9715vwIdu0taWXSySOODQBhRrADEAuHRpxWteampFqbUzUfz7Is9XU7S57QFQsgKgh2AGLBbVVb19Usq8alTlwb17VJkg4OE+wARAPBDkAsuOvN9Xr2ea2Vuy3Z0NisZufyvh0XAOqFYAcg8qZncxqdmJMkreusfXydy7stmdvVCwBhRrADEHnertLeLv9a7Lytf4OsZQcgAgh2ACLPO2t1nY9dsb2e/WYPj9JiByD8CHYAIu+QZ505bxirVUdbk1JJZyLG4BgtdgDCj2AHIPLc0NXemlY6lfTtuAnLUlebExTpigUQBQQ7AJE3VAp2Pe3+tda5ujucY7JfLIAoINgBiLyh8VlJUk9HHYJdKSzSFQsgCgh2ACKtWCxqaMwJdt0+TpxwuS1241NZ1rIDEHoEOwCRNjWb02zWCVz1abFrKt+m1Q5A2BHsAESad1JDPYLdpv728u3RyTnfjw8AfiLYAYg0d3ydVJ9g1989373LBAoAYUewAxBpw57u0e4O/8fYdbVllLCctewIdgDCjmAHINLcFruEJXW1NR3l2auXSFjqLB13gP1iAYQcwQ5ApLkTGrraM0okrLq8R1dpAsUg24oBCDmCHYBIKy91UofxdS539wm6YgGEHcEOQKS5u05012HXCZfbYjc8PqtsrlC39wGAWhHsAERWoVjUcB13nXB1eULj0DitdgDCi2AHILLGJueULxQl1TfYeVsD6Y4FEGYEOwCRNTaVLd/uqcN2Yq4u7+4TBDsAIUawAxBZ9V6c2NXZ2qTSUnYamZhd/skAEKCUnwczxiQk/VzSeZJ+aNv25X4eHwC8vK1n9ZwVm0wm1NnWpNGJObpiAYSa3y12fybpdJ+PCQAVucEulUyovSVd1/fqKe1qcXiEYAcgvHwLdsaY4yV9QNJ7/DomACynvNRJR5Msqz6LE7t6O50WwQEWKQYQYn622H1G0oOSrvbxmACwpMEGrGHncsfwDY/NKl9gLTsA4eRLsDPGvFnSJZLebNs2VzwADeF2xdZzfJ3L7Yr1rp0HAGFT8+QJY8xmSR+T9M+2bd9be5GO1NPTWo/DRlYy6eRx6mUh6qWyuNZLNlfQ6KQTsNZ1Npf3iU2nE0qnk0t+L0mWJVnSil+TTifU39NSfv1sIX71KcX3XKkV9VIZ9RJOfrTY/ZukQUl/78OxAGBFhsdmVHTWJlZ3R/3WsHP1ds2/x8Aw4+wAhFNNLXbGmFdKerGkK2zbrtuVbnh4ql6HjiT3ryPqZSHqpbK41svje0bKtzta0yqUdqDIZgvKZvOy0smK30tSsSgVpRW/JpstLJh1+8S+UQ0f31vXzxeEuJ4rtaJeKqNejtTf3xF0EaoPdsaYjKR/lfRfknYZY05c9JSW0n3jtm0frKGMAHAEd+KE1JgxdumUs6TKxHRWh8dY8gRAONXSYtciqV/ScyQ9WuHxC0r3f0XSa2t4HwA4wpA32Hm2/Kqn7o6MJqazbCsGILRqCXaTkl62xGPXS7pf0vsl7a7hPQCgopHJOUlSJp1Uc5Ovm+gsqbu9SXsOsV8sgPCq+mpo23ZW0jcqPWaMkaRDtm1XfBwAajU05syI7WxrTGudJPV1OzNjhydmVSwW674oMgCslt9bigFAQ7itZl0N6oaV5teyy+YKmpzJNex9AWCl6tJ/Yds2f8YCqCt3jF1na+OCnTdEDo/P1n1/WgBYLVrsAETObDaviemsJKmzrf4zYl3e2bfsPgEgjAh2ACLHOyO2s4Fdsd49aYfHmUABIHwIdgAix9ta1siu2M62JrnjTGixAxBGBDsAkbMg2DVwVmwqmVBbaVwdwQ5AGBHsAESOtyu2o4EtdtJ8kByeINgBCB+CHYDIcVvL2ppTSqcaexkrBzta7ACEEMEOQOQMlUJVV3vjZsS63GA3QrADEEIEOwCR47aWeZcfaRS363dyJqfZbL7h7w8AyyHYAYiccrALoMWuyzNZg1Y7AGFDsAMQKXOexYmDaLHbsK61fHu8VA4ACAuCHYBI8c5G7Qkg2PV2NpdvD9FiByBkCHYAImV4bD5MBdIVu2i/WAAIE4IdgEjxhqkgumKbm1LKpJOSFq6nBwBhQLADEClDnj1auxq4T6xXB2vZAQgpgh2ASHHDVHNTUs1NqUDK0NHqbCtGix2AsCHYAYiUINewc3W20mIHIJwIdgAiZSjANexc7iLFIxOzyhcKgZUDABYj2AGIlCAXJ3a5Y+yKRWl0Yi6wcgDAYgQ7AJGRyxc0NukEqWC7YtPl23THAggTgh2AyPBu4dUd0IxYab4rViLYAQgXgh2AyBjzbOHV7dkBotE62gh2AMKJYAcgMrwhqifAMXatmZSSCUuSNDrFGDsA4UGwAxAZQ2PB7jrhsiyrvDgyLXYAwoRgByAy3F0nMumkmpuSgZalq9Ri6N27FgCCRrADEBlDo06w62pvkmVZgZalqzTOzrvFGQAEjWAHIDLcLby6Ahxf5/K22BWLxYBLAwAOgh2AyHB3negKcKkTl1uGbL6gCc9sXQAIEsEOQCTk8oXyOnZB7jrh8rYaMoECQFgQ7ABEwtjknNwOz1C02LGWHYAQItgBiIQhT3jqagu+xc7bajg8QbADEA4EOwCRMByS7cRcHa1pufNyWfIEQFgQ7ABEwvDY/LIiYZgVm0wm1NaSlkSLHYDwINgBiAR3665U0lJrcyrg0jg629h9AkC4EOwARIK7nVhHa/CLE7vcYDdCsAMQEgQ7AJHgBrvOtuDH17n6ulsk0WIHIDwIdgAiwd26q6M1PMGuu8MZ6zc1m9PMXC7g0gAAwQ5ABBSKxXJ3Z2eYgh2LFAMIGYIdgNAbn8oqX3CWJ+5oSwdcmnneZVcYZwcgDAh2AELPG5raW0LUYtcx32I3RLADEAIEOwCh510nLkxj7Lzr6Y2wlh2AECDYAQg9b2gKU1dsJp1Uc1NSEmPsAIQDwQ5A6LldsZak9pbwBDuJRYoBhAvBDkDouaGpvTWtZCJcly2CHYAwCdcVEgAqGJlwthPragt+j9jF3OVXCHYAwoBgByD03NDU1R6eiROuzlKZxibnlMsXAi4NgLWOYAcg9NzJE10d4W2xK0oaLbUsAkBQCHYAQi2bK2hiOitJ6grRPrEu7961wyx5AiBgBDsAoeZd6sS7hVdYbOxrK98em8oGWBIAINgBCDnvpIQwjrFb19lcvs22YgCCRrADEGreFruuELbYtTanlExYkqShsZmASwNgrSPYAQg1bytYGLtiLcsqj7Njv1gAQSPYAQg1dw27VNJSa3Mq4NJU5u5fS4sdgKAR7ACEmjvTtKstI8uyAi5NZR2tzjZnLFIMIGgEOwCh5oalzhBOnHDNbys2o2KxGHBpAKxlBDsAoTbiabELK7crNpcvanyaJU8ABKfqASvGmD5JH5X0dElbJbVJ2ifpV5L+0bbte30pIYA1q1gslidPhHGpE5cb7CRpeGy2vBsFADRaLS123ZJOkXSrpL+X9DZJX5F0kaQ7jDFX1F48AGvZ1GxOczln/9XIBDt2nwAQoKpb7GzbfkzSBYvvN8Z8StITkt4l6QfVFw3AWudd6iTMXbELthVjAgWAANVjjN1BSVNyWvQAoGrj07ny7e6O8Aa79pa03Am7BDsAQap5UShjTFpSV+lYx0r6C0kdkr5T67FdPT2tfh0qFpJJJ49TLwtRL5VFuV6mdw6Wb/d2ZpRIWEqnE0qnk0qUdntY7feSZFmSJVV9jMXfZzJJdbQ2aWxyTlNzuUjWtRTtc6WeqJfKqJdw8mO1zwsl3eb5flTSRyS934djA1jDhkbnF/wN43ZiXt3tGY1NzmlwlEWKAQTHj2B3r6QrJGUknSzpdXJa7DKScsu8bsWGh6f8OExsuH8dUS8LUS+VRbleDgxOSpJaMkmlkgkVCkVlswVls3lZ6aQkrfp7SSoWpaKkQqFY1TEqfe+OsxsYno5kXUvRPlfqiXqpjHo5Un9/R9BFqD3Y2bY9LGdmrCR91xjzH3LC3vGSnl/r8QGsXe7kic4QT5xwubN2GWMHIEi+T54oBb1vS3qeMWa738cHsHYMuWvYtYV3qROX21U8PZvT9KwvnRUAsGr12nmipfS1p07HB7AGDI+Ffzsxlzd8jrCWHYCAVB3sjDEblrh/u6TfkzOJ4qFqjw9gbcsXChqdDP92Yi7vAspDdMcCCEgtY+zeVdpd4nuSdskZi3yqpNdLapf0Btu2mR4GoCpjk1kVnbkNCxYADivvrN0Rgh2AgNQS7L4jaYukl0paXzrW/tL9/2Lb9q9rLx6Atco7CSHM24m5vF2xtNgBCEotW4rdqvnZsADgK+84tSi02DWlk2rJJDU9m6fFDkBg6jV5AgBqsqDFLgLBTppfloUlTwAEhWAHIJTGp7KSnO2/OlqjEew6WtOSCHYAgkOwAxBKQ+PO3Kv2lvSCfV7DzO0yHma5EwABIdgBCKWhMSfYRWF8nau/21nCc2xyTrl8IeDSAFiLCHYAQmnQXZw4It2wktTT0Vy+zSLFAIJAsAMQOsViMZItdt0d82VlnB2AIBDsAITO+HRW2ZzTldkZgV0nXN5Figl2AIJAsAMQOu4esVLEWuwIdgACRrADEDrujFgpWsGutTmlVNKZwUuwAxAEgh2A0BmKaIudZVksUgwgUAQ7AKHjttglEpbamtMBl2Z1OttKixQzKxZAAAh2AELHHWPX1dYUmcWJXeUWuzGCHYDGI9gBCJ2hUjdmd0d0ZsS6Okvbio1MzKpQLAZcGgBrDcEOQOi4a9h5Z5lGhdtily8Uy/vdAkCjEOwAhEqhWCxPPOjpjGKwm5/sMcIECgANRrADECrjk3PKF5wuzJ4Ittht7Gst3x6bmguwJADWIoIdgFAZ8rRydXv2Xo2KXk+ZhycIdgAai2AHIFTc8XWS1BPFyRNtTbJKE3m9nwUAGoFgByBUFrbYRS/YJRKW2ltKa9kxxg5AgxHsAISKu/5bMmmpvTVaixO7OludCRS02AFoNIIdgFBxd53obs8oYUVrcWJXR2lmLC12ABqNYAcgVNx9YqO4hp2rgxY7AAEh2AEIlXKLXQTH17ncYDczl9f0bC7g0gBYSwh2AEKjUChqZNxZIiTaLXbzYwOH6I4F0EAEOwChMTo5V95fNYpLnbjYfQJAUAh2AELDu1NDFLcTc7ldsdJ81zIANALBDkBoeLsteyK464TLG+xosQPQSAQ7AKExODrfuhXlyRPpVEKtmZQkp3sZABqFYAcgNNylTlLJhNqaUwGXpjZd7axlB6DxCHYAQsNd9627o0lWRBcndnWVZvUyKxZAIxHsAITG4Nj8rhNR57bYsUgxgEYi2AEIjaEYBTv3M4xPZTWbzQdcGgBrBcEOQCjk8oXyDNIoT5xwedfho9UOQKMQ7ACEwsjErIql2243ZpT1dM4v1+Kd7QsA9USwAxAK3tmjceiK9bbYDdJiB6BBCHYAQiEua9i5utqa5E7sJdgBaBSCHYBQ8IafKO8T60omE+os7UBBVyyARiHYAQiF4Qlnh4bmpqSam6K9OLHLXcuOYAegUQh2AELBDT9dbdFvrXN1lyaB0BULoFEIdgBC4fDotKR4zIh1re9tlSQNj88pXygEXBoAawHBDkDgisXifItdDGbEutZ1OUueFIpF9owF0BAEOwCBm5zJaWbO2Z2hqy0+LXY9HaxlB6CxCHYAAucNPZ0xarHr7fTuPkGLHYD6I9gBCJx3ckGsWuw8u08cZgIFgAYg2AEI3IJgF6MWu0w6qdaMs3QLXbEAGoFgByBwbuhJJxNqa47HGnaurtJiyyx5AqARCHYAAueGnu7OjCx3H66YKK9lR4sdgAYg2AEInBt64rCV2GLdpa7lobEZFYvFgEsDIO4IdgACN1Ra463XM9kgLtwxg3O5gsansgGXBkDcEewABGoum9fYpLNPbByDXbdnMgjj7ADUG8EOQKC8Yce77ltcbF7fVr49WgqwAFAvBDsAgfIGO+9ODXHR19VSvn2YCRQA6oxgByBQ3h0Z4thi19acUjrlXGoJdgDqjWAHIFBu2LGshePR4sKyrPJuGoOj0wH33LwOAAAgAElEQVSXBkDcVb0SqDHmJEmvkXSFpBMkdUjaLelWSR+2bXu/LyUEEGvuUiddbU1KJuP5t2ZXe0aHR2dosQNQd7VcRd8o6Z2S9kn6R0l/LumXkt4q6QFjzCm1Fw9A3Llj7HpiOCPW5bbYHR6hxQ5AfdWyd883JH3Etu1hz33XGGN+KenTkt4v6eW1FA5A/A2NxXdxYpe7lt3kTE7Tszm1ZOK1bRqA8Ki6xc627TsXhTrX10pfz6j22ADWhkKhqOHS4sRxnBHr6iptKybNB1kAqId6DGjZUvp6qA7HBhAjIxOzyhecbbbiOCPW1dnGIsUAGqMe/QEfLH39vF8H7Olp9etQseAOMKdeFqJeKgtzvRzwTCbo625RImEpnU4onU4qkbAkyffvJWcGriXV7T0Wf9/XPd8aOZ0thPJnIYX7XAkS9VIZ9RJOvrbYGWP+VtJLJN0k6Yt+HhtA/Ax4JhPEcTsxV0drUznkDTCBAkAd+dZiZ4z535L+QdLtkl5j23bRr2MPD0/5dahYcP86ol4Wol4qC3O97D04Xr7d3Z5RoVBUNltQNpuXlU5Kku/fS1KxKBXljPGrx3ss/j6fL6q7vUlDY7PaNzAZyp+FFO5zJUjUS2XUy5H6+zuCLoI/LXbGmHdI+rikH0p6gW3b/JQBHJW7rltrc0qZpuRRnh1t60otkgf5RxBAHdUc7Iwx75T0z5K+L+l3CXUAVspd1y3OS5241pX2jD00xCUSQP3UFOyMMe+S9BFJ35H0e7ZtM90LwIoNroE17FzrupwWu8mZnCZnsgGXBkBc1bKl2FslfUjSQUk3SnqZMcb7lAnbtm+qrXgA4qpYLOrwiBPsutdQsJOkQ8PTOm5TOsDSAIirWiZPnFv6ukGVlzbZLWd2LAAcYXRyTrPZvKT5bso48wa7gZFpHbepM8DSAIirqoOdbdt/IOkPfCsJgDXl0PD8sh99XfFd6sS1uMUOAOqhHjtPAMBReWeH9nXHv8WuuSmltmbnb+lDrGUHoE4IdgAC4bZaWZbUuwbG2ElST2nJkwFa7ADUCcEOQCAG3IkT7Zny1kRxt6HXWdB1YJRgB6A+1sbVFEDouF2xPR3xH1/n6i91OQ+PzSqbywdcGgBxRLAD0HDFYlEHSwv19naujW5YSerrdkJsUfMtlgDgJ4IdgIYbm8pqZs5psertXDstdn2eZV2YQAGgHgh2ABrukGdG7FrqivXO/h0g2AGoA4IdgIY7ODQfatZSV2xHa1rplHPZZWYsgHog2AFouEMjToudZTmzYtcKy7LK++LSFQugHgh2ABrOXcOut6N5zSx14nLHFNIVC6Ae1tYVFUAouF2x7izRtWQ+2M2oUCwGXBoAcUOwA9BQxWKx3A3Z39MacGkazx1TmMsXNDI+G3BpAMQNwQ5AQ41PZTU9m5M0v2DvWrKlv718e4hgB8BnBDsADXXIMxt0LXbFrve0UjKBAoDfCHYAGuqgZw27tdhi19uZkWU5t73LvgCAHwh2ABrqYKnFzpK0rmvtBbtkMqGuttKSJ56QCwB+INgBaCg3zHR3ZMqL9a415bXsWKQYgM/W5lUVQGDcMLMWu2FdbrA7SIsdAJ8R7AA0TLFYLHfF9q3lYFday25qJqeJ6WzApQEQJwQ7AA0zMb22lzpxuS12EjtQAPAXwQ5Aw3jXbevvWcvBbn6Zl4NDdMcC8A/BDkDDHPRMFljLwa63s1mlFU8W1AkA1IpgB6Bh3NYpS1LfGlzqxJVOJcp7xu4bnAy4NADihGAHoGEOlIJdV/vaXerEtb7XCbb7DhPsAPhnbV9ZATTU3gEnxLihZi1ztxY7MDilfKEQcGkAxAXBDkBDFApF7S+1Tm3obT3Ks+NvQync5gtFFioG4BuCHYCGODQyrWzeaZnaSLBbEG73HWZmLAB/EOwANIR3LBktdvNdsZK0nwkUAHxCsAPQEHsJdgu0ZFLqaE1LItgB8A/BDkBDHBh0uhs7WtNqyaQCLk04uNuq7RukKxaAPwh2ABrCbbFby1uJLbalv12SE3oLxWLApQEQBwQ7AHXnnRHbR7Ar27jO6ZKezeY1PDZ7lGcDwNER7ADUnXdGLC1287yzgxlnB8APBDsAdeedEUuwm7dxXVv5NjtQAPADwQ5A3XlnxK7lPWIX62hNq7kpKYkJFAD8QbADUHdua1RXe5OamRFbZllWuQWTrlgAfiDYAag7N7Rs8nQ9wjEf7GixA1A7gh2AunJmxDqhxZ0Finl9PU6wm5jOamxqLuDSAIg6gh2Auhrw7hFLi90Rjt3YUb49MDITYEkAxAHBDkBdeSdObKLF7gibvTNjGWcHoEYEOwB1tXCPWFrsFuvtbFYq6VyK9w4Q7ADUhmAHoK72e2bEtjYzI3axRMLSuq5mSaxlB6B2BDsAdeW22G2ktW5JfeVgNxFwSQBEHcEOQN0UCsXyMh7MiF2au3/u0NisJmeyAZcGQJQR7ADUzfDErHKlGbGb+mixW4q3NfOJA+MBlgRA1BHsANTNHmbEroi3NfNJxtkBqAHBDkDd7NrvtD5ZosVuOR2tTepsa5I0X2cAUA2CHYC62blvVJK0vrdVzU3MiF3Oln4n+O46MBZwSQBEGcEOQF0Ui0U9vs8JKVvXtwdcmvDb2u/U0YHBKU3P5gIuDYCoItgBqIvB0RlNTDszPI8h2B3VFk8dPXGQ7lgA1SHYAaiLXZ7ZnbTYHZ23jnYfZD07ANUh2AGoCzecWNb8+DEsrautSW2lnTl2M84OQJUIdgDq4vH9TjhZ392qdCoZcGnCz7IsHbOhQ5L0BC12AKpEsAPgu0KxqF2lYMcyJyt3bCnY7Ruc1OxcPuDSAIgigh0A3w0MT2uqNLNz0zqC3Uods8EZZ1csSk8eotUOwOrVtLCUMeZvJJ0l6WxJJ0gq2LbNYlXAGud2w0rSpj52nFipres7yrd3HRjTiVu7AiwNgCiqNYR9WNKIpLsltUvqr7lEACLPnRGbTFpa30OwW6nezoxaMilNz+a0myVPAFSh1q7YE23b7rFt+1mSbD8KBCD63PF1m/valEoy4mOlLMsqj0ncfYBgB2D1arri2ra9w6+CAIiHQqFYXurEnQyAldtcCnb7Dk9pLssECgCrw5/SAHy1f3BSs6VAQrBbveO3OOPqCsWiDgxPB1waAFETiYkOPYzRWSBZ6tqiXhaiXiprdL3cvXOwfHv75k6l0wml00klEpYkBf695CyabEmhKZP3++M2zYfh/cNTOtOsr/pnsVr8DlVGvVRGvYQTLXYAfLVjz6gkKZ1KaOM6Lvir1dfdouYmZ0Fnty4BYKUi0WI3PDwVdBFCxf3riHpZiHqprNH1Yu8akiRt6W+XJUvZbEHZbF5W2gkrQX8vOevEFeWMBwxDmRZ/v3Fdm3btH9NDu4Yaej7zO1QZ9VIZ9XKk/v7gh5/QYgfAN7l8QU+UFtZ1F9vF6rljE/cOTGpsai7g0gCIEoIdAN88OTCpbK4gSdq+qTPg0kTXMZ5JJ48+SXcsgJUj2AHwjf3EcPn2SVu7AyxJtG3tbytPrNixj2AHYOVq3VLsdZK2lb7dJskyxrzHfdy27Q/WcnwA0fLQLifY9XU1q7sjE3BpoiudSuqY9e3afWBc9hMjQRcHQITUOnnijZIuXXTfBzy3CXbAGpHLF/TIk04IcddiQ/WO29yp3QfGtevAmKZnc2rJRGKuG4CA1XSlsG37Mp/KASDidh8cLy9MfALBrmbHb+7S7XftVbEo7dg7qqccvy7oIgGIAMbYAfCFt8uQYFe77Zs65S6pbD9JdyyAlSHYAfDFI3uc8NHdkWF8nQ9am1Pa0OusE/YowQ7AChHsANQsXyjokVKL3Tb2h/XNicc4M4t37h9TNpcPuDQAooBgB6BmTxyc0MycEzyO3Uiw88tJpWCXyxe1c99YwKUBEAUEOwA1846v27aBhYn94h2r+Aj7xgJYAYIdgJo9XFqYuIfxdb7qbGvSus5mSSovJQMAyyHYAahJoVDUo6XWJLfrEP7Ztsnp2n5s76jyhULApQEQdgQ7ADV54tC4pmdzkqQT2UbMd2ZbjyRpdi6vfYNTAZcGQNgR7ADUxDu+7sStrF/nt5M9raAPPj4UYEkARAHBDkBNHtrtjK/r7shoXVdzwKWJn3VdLeV6/Y09EHBpAIQdwQ5A1XKFoh7a5QS7U7b1yLKso7wC1TDHON2xO/aOamRiNuDSAAgzgh2Aqt2/c1DZvDOg/8wT+wIuTXydfOx8d+z9dMcCWAbBDkDV7nz4kCQpk07q5GN7Ai5NfG3pb1dHa1qSdBfdsQCWQbADUJVsrqC7H3FCxqnbe5ROcTmpF8uydNr2XknSbx8f0sxcLuASAQgrrsQAqvLQ7uHyNmJPOX5dwKWJv9NLdZzNF/TATrpjAVRGsANQlXseOyxJSiYsnbKdbth6O+mYrnKr6N2PHg64NADCimAHYNUKhaLuKnXDHre5U81NqYBLFH/pVFKnlrpj7985yC4UACoi2AFYtcf2jmpsck6SZI7tDbg0a8cZJzrdsRPTWT365GjApQEQRgQ7AKvmttZZ1sKlOFBfpx+3TonSUoF0xwKohGAHYFWKxaLuetQJdsdv7lJbczrgEq0dbS1pHbuxQ5J096MDKhaLAZcIQNgQ7ACsyhMHJ3R4ZEaSdOZJLErcaE87qV+SdHh0RrsPTQRcGgBhQ7ADsCq//O2B8u0z2G2i4c45dYMSpa3bbr97b8ClARA2BDsAK2YlLP38ASfYnbi1S72dzQGXaO3pbGvSScc44xp/9eABTc+yWDGAeQQ7ACt21yMDGp/KSpLOf+qmgEuzdp11stMdO5st6A77UMClARAmBDsAK3bbXU7XX0smVR7rhcY7fnOXujsykuiOBbAQwQ7AigyMTOuBnYOSpKebfvaGDVAiYencUzdIknbtH9fuA+MBlwhAWHBlBrAiP3tgftLEeadvDLAkkKRzT9ug0hwK/eT+/cEWBkBoEOwAHFW+UNBP7t0nSdrS16ZNfW0BlwjdHZnyFmM/v3+/ZufyAZcIQBgQ7AAc1f07hjQ8PitJetrJ6wMuDVwXlCawzMzl9euHDwZcGgBhQLADcFQ/uc9prcukkzr9OPaGDYvTj+tVe4uz88cPf7OHnSgAEOwALO/Q8JTueczZl/Tpp6xXUzoZcIngSiYTuujMzZKcHUF+u3s44BIBCBrBDsCyvvXTx+U2BF38tM3BFgZHuOzsLcqUwvZNP95Jqx2wxhHsACzp4PC0fvmgM3braSf3a0t/e8AlwmKtzWk9o7T0yY59Y3r4yZGASwQgSAQ7AEu68cc7VJRkWdILLtgedHGwhPNO30irHQBJBDsAS9h9YFx3PORsV/V0s14belsDLhGW0pJJ6cIznBmyj+4Z1UOMtQPWLIIdgIq++dOdkqSEZemKc48NuDQ4mkvP2qKmtHNJv/nnu4ItDIDAEOwAHOHRPSO67zFn+7CnndyvdV3NAZcIR9PWktbFpRmy9hMjemjXUMAlAhAEgh2ABQqFoq6/bYckKZ1M6KIzmAkbFc865xg1lfbw/cqtj6q85xiANYNgB2CB/7rjST22d1SSdMlZm9XZ1hRwibBSHa1Neu4zt0mS9h2e1HfokgXWHIIdgLKB0Rl988fO2Lq+7hY9//ztwRYIq/asp2/Vhh5nosu3f7pT+w5PBlwiAI1EsAMgyemCvebbDyqbL8iS9NrnGnaZiKBkMqEXXLhdlqRcvqgv3WKrwPInwJpBsAMgSfrBb/ZoR7kLdouO39IVcIlQrc197eWtxh55ckQ/vndfwCUC0CgEOwDaMzChG25/TJK0rrNZz3smy5tE3fOeuU09HRlJ0vW37dCBoamASwSgEQh2wBo3Njmnq2+4X7l8UZYlvfCi45VO0QUbdZmmpF7+7JMkSdOzOf3LN+7TTDYfcKkA1BvBDljDipb0yW/er4GRaUnSlRds19b17AcbF6cfv07PPc9pfT04NKV/uf5eZXOFgEsFoJ4IdsAaVSwW9dmbf6tH9zjj6s49bYOeww4TsXPlBdt12nG9kpyFi790i81eskCMEeyANermn+/WLx44IEk6fnOnXnH5ybJY0DZ2LMvSiy48Xts2dkiSfnb/fn3rp7sId0BMEeyANaZQLOqGH+3UTT9x1qvr6cjoDVeeqnSKy0FcpVIJ/eHvnlbeGu7bP3tcX7rFVr5AtywQN1zJgTVkLpvXv9/0gL77i12SpPaWtF55+clqa0kHWi7UX3tLWv/rJU8tz5T90T379MkbH9AsEyqAWCHYAWvE2NScPvbVu3WnPSBJ2tDbqne86iyt62oJuGRolPU9zs98S3+bJOmexw7rn752j0YnZgMuGQC/EOyANeDOhw7qfZ//tXbsG5Mknbi1S3/+yqepr5tQt9Z0tWf09pc/Tcdvdhag3rF3VO/9wh26b8dgwCUD4IdU0AUAUD9TMzlde/29+u87nyzfd86p6/Xq5xilkvxdt1a1ZFJ6xbNP0g/ufEJ3PnRIY5Nz+vj19+qxfWN6/ZWnBF08ADUg2AExlC8UdKc9oG/ctkODYzOSpOampC5/xrG69KzNhDoomUzoFc8+Sads69X1P3xUs9m8vvOzx3XfY4f14gu362kn9TFLGogggh0QI7l8Qb948IC++4vdOjQ8Xb7/lG09euUVJyuXK/KPNcosy9K5p23QcZs79fmbH9TegUk9cXBcV994v7Zt7NCLLiDgAVFTc7AzxrxE0jslPVXSnKSfSnq3bdv31XpsACtzcHhKv3zwoH563/5yC53kzIR8wYXbddGZm1UsSoeGppc5Ctaq/u4Wvf75p+q+HYf133fu0fRsTrsPOAFv07pWXfTUTTr/KRvV3Z4JuqgAjqKmYGeMeaOkz0p6QNJfS8pI+hNJPzPGXGTb9r21FxHAYoViUXsOTejh3cO669HDeuTJkQWPd7U36fJnHKsTt3Rr8/o2WZbFgrRYVjKR0HPO26bLzt6q236zR/9955Oamctr/+CUrr99h2740U495fhePeW4Xp20tVvHrG9XIkFLHhA2VQc7Y0yPpKsk7ZF0oW3bY6X7vy7pt5KulnSJH4UE1rqZOacFZef+MT2+b1wPPzGsiensEc/b0tem85+6Sec/dZPSqQQtdFi1lkxKz3vmNp22rVcPPzGkOx46pINDUyoUi7pvx2B59mxLJqkTt3bLHNOtEzZ36rhNnWpKJwMuPYBaWuxeLKlT0lVuqJMk27b3GGOuk/RGY8x227Z31VhGIPYKhaImprManZzT6OSshsZmdWBoSgeHpnRweFr7Bye1VINbd0dGTzfrdd7pG5SwEuruaGIXCdSsOZPSZWdv1RXnHqtd+8f147v3yvb8QTE9m9f9OwZ1fynoJZOWtvS1aUNvq9Z3t2h9T4t6OjLqbG1SV3tGHS1pWviABqgl2J1X+vrzCo/9XNIbJZ0raVcN77EmTM5kVSg4/2of8W+3519z91axNKNxuUVFl+p2W3x30fuORzy25EMVy1XpjsWPLdsbuNTrFr2ouMQ347M5FSWNj82WXrb0my1XjuKiUi/33HyxqFyuoGy+oHzpay5fUC5fnL+dc76fzeY1PZvT1GxO07N5zczlNDmT09RMTlOz2WXfx9sS0tXWpOM2d+qErV1a19GibZvalWlyfpUHhmZkWZI71j1hWeXvLWvh967F98X9e+e+Up2s6TpY+jnecyWRsHT8lk51tDTpVc85SWOTWe3cP6qHHx/WvsFJDY7Oj+k8ODytg8OVW4ktS6WQ16SOliY1NyWVSSeVTiXUlE4qk06oKZ1QOpVUUzKhZNJSMpFQMmEpmbSUSDj3pRKe+xOWEgnL+TmWPohV/t/8+y6+f/Hzy0+3JEvWgnqyLEuFhCXLsjQ2OXvE69cyK+Vcl8am5gIuyUKtmaRSybXbemxVO+7GGHOzpN+VdJpt2w8teuw5km6R9Be2bV9VZdn6JR2q8rUAAABBWS9pIIg3rqW/prX0tVKz0cyi5wAAAKDOaumKnSp9rTT/vXnRc6pxWE7irfU4AAAAjeA2aB0OqgC1BLs9pa9bJT206LGti55TjaICasYEAACowmTQBailK/bXpa/nV3jsgtLXO2o4PgAAAFahlmB3k6RxSW82xnS6dxpjtkp6uaSf2rb9eI3lAwAAwApVPStWkowxfyTp03J2nvi0pCZJfypnRuvFtm3f7UchAQAAcHQ1BTtJMsa8VNJf6ci9YtlODAAAoIFqDnYAAAAIB/YdAgAAiAmCHQAAQEwQ7AAAAGKCYAcAABATBDsAAICYINgBAADEBMEOAAAgJgh2AAAAMUGwAwAAiIlUEG9qjLlC0ksknSXpDEktkl5n2/a1VRzrJZLeqSO3NLuvwnNTkv5C0v+UtF3SoKRvSXqPbduDVX0Ynxljtkn6sKQrJLVLekTSJ2zb/swKX/8+Se89ytO22ra9t/T8yyTdtsTzfmPb9jkred96qrVOSsdYbouVDtu2JxY9v1XS30l6paRNkvZL+pqk99u2PbW6T1AfPpwrJ0l6Ten1J0jqkLRb0q2SPmzb9v5Fz79MITlXVvN7v8TrV/Xz9eMcbIRa6sUY80JJL5Z0vqRjJc1IekzSZyR9ybbt3KLn/4ekNyxxuD+1bfsTVX4M39VYL38g6QtLPHyDbdsvrfCaMyT9g6SL5Oyhfr+kj9q2fWNVH6AOaqyTXZK2LfOUW23bvsLz/P9QBM4VY8zfyMklZ8u5JhZs2151Tgr6+hJIsJPzj8lrJP1Wzgl/bjUHMca8UdJnJT0g6a8lZST9iaSfGWMuqrBf7RckvVbSdyT9k5xw92eSLjHGPNO27fFqyuEXY8xWSb+U1CXp45Iel/RCSdcYY461bfv/rOAwN8q5GC+2TdIHJd3lhrpFrpH0k0X3BR52faoT10/kfM7FZha9Z1LS9yRdKunLkn4s5+L3l5LOM8Zcbtt2frWfxU8+1csbJb1d0nclXS9pStIzJb1V0muMMRfatv1whdcFeq5U8Xu/+PWr+vn6fA7WTa31IifATcv5Y/dBSZ1y/mH6nKSXGGNeaNt2pT+QXlfhvl9X9yn850O9uD4k6aFF9+2u8H5nyglJs5L+WdKAnH93bjDGvMm27c9V9UF85EOd/JmcALLYayU9V9K3l3hdqM8VOeFqRNLdcj5f/2oPEIbrS1DB7t2S/ti27ZnSX0OrDnbGmB5JV0naI+lC27bHSvd/XU5gvFrSJZ7nP0vOSfdt27Zf7Ln/15JukvRXchJ2kD4kaaOk3/f8ZfcZY8yNkt5ljPmSbduPLneA0l9blVorP1C6WSnYSNIvqmkxbYCa68Rj5wo/4xvk/FJebdv22907jTE75fzivUHS51f8CerDj3r5hqSP2LY97LnvGmPMLyV9WtL7Jb28wusCO1dW+3u/hNX+fP08B+vCp3p5raQfesObMebjkm6X9AJJz5fzD9YCIb1uSPKtXlw/sG379hU872pJbZJ+x7btO0vv9zlJP5d0lTHmBtu2R1b1QXzkR53Ytn1TheMm5DQezMgJNJVeF9pzpeRE27Z3SJIx5nZVEewUgutLIGPsbNvea9v2zNGfuawXy/mL8rPuiVk69h5J10m62Biz3fP815e+XrWoLN+S08L1egWo1HT7UkmPV2iuv0pSUk4rZzXHTkr6Q0mTkr66XBmMMc3VvEc91KNOjDFNxpiOozzNPRf+edH9n5ZTh7E4V2zbvnNRqHN9rfT1jOXKENC5strf+0pW/POt5++lz2quF9u2b13cIldqWbi+9G3F88EYYxljOkvXmbDx43wpM8a0G2Oalnl8u6SLJf3IDXWl98tJ+tdSWX5vlZ/Bb77Wicdz5PQMXb9UcA35uSI31NUo8OtLlCdPnFf6+vMKj7n3eVsCz5NUkNPkudgvJG0zxqz3r3ir9lQ5Yw1/UeGxX0nKq8ouazl/aW+R9HXvL/Ii/yLnpJs2xjxujHmPMSZd5fv5xe86eamc7sYxY8ygMeazxpgN3icYYyxJ50jaZ9v2gm6W0h8jd0k6p/S8oNTzXJGcc0WSDi3xeJDnymp/7xeo4udb77r2S031chRHOx9GJI1KmjHG/MgY8+wq36ce/KyXb0kalzRrjPmtMeZ/VbgO1PPn4Jd6lfFNpa/LjQsL87lSs7BcX6Ic7LaWvu6p8NieRc9xbx+2bXt2hc9vtCU/j23bWTkX1WrL9+bS10rdsFk5Yw7/RtKLJL1F0pOSPiDpplLzelD8rJM75HQtvlTOOI/vyJlE86tF4a5XTjdKpfPKLUubpJ4Vvm891PNckZzuFOnI7uYwnCur/b1fbLU/33rXtV9qrZeKSuN/3iJpWM6QFa+Dclqh3ianFep9kk6T9ANjzKtX+1514ke9TMlpxf4LOWOf/kRSTtK/SfpUHd6v3nwvY6lR5EWSHrZte/H4Wyka54ofQnF9qXqMnTGmXc4FfqVusG377mrfr4LW0tdKQW1m0XPc25W6nZZ6flVqqJflPo/klHHV5TPGbJJ0paT7bdv+1eLHbdv+mZyLlfc1n5FzIXu5nCB03Wrfd9HxAq8T27YX/9VzrTHmV5I+KWcW8VtL96/kPd3nDa3kvZcShnqpUKa/lTNj/SZJX/Q+1ohzZQVW+3u/mtcvPsbQCp9f83XDB7XWyxFK5+e35HTb/b5t2wvOd9u2/3rRS75lnNmP90u62hjzTdu2p1fznnVQc73Ytn2dFp3XxphPS/qRpLcYY77gubYu+X62bc8ZZ3Z+0OeL7+eKpD+QlNYSrXUROVf8EIrrSy2TJ9rlTIJYqcfkzDTxiztlOFPhseZFz3FvV3ruUs+vVrX1stznkZwyHq6iPH8o5+e84mnTtm0XjTHuwPkXqPZ/rMNWJ65PyfnL8QWe+1bynt7n1SJU9WKM+d9ylmi4XdJrlpgBuUAdzpWjWe3v/WpeX+kY9T4H/VJrvSxQCnXfk7P0wxA7gF0AAAXXSURBVJ/Ytv3NlbzOtu29pYkCfylnhvVSy+M0iq/14rJtO2eM+ZCcFuwr5XSbLft+pbF5VjXv57N61Mkb5SyZ8qWVviCE54ofQnF9qTrY2bZ9QM5JGhRvk/HiKeiVmjf3SDrZGJOp0B27XNP0qtRQL0s2gZfGL62XdM9qDljqx3+jlpmltIxdpa/VzApaIEx1sqhcRWPMbkmne+4ekvPLtlTz91Y548uWav1dzfuHpl6MMe+QM9j3h5JeVGmtpWXsKn2t+VxZgdX+3i+22p9vXc9BH9VaL2WlyUX/KekCSW+1bfvfV1mWXaWvjTgfjsa3eqlgV+mr93Mu15Xp278zNfK1Towxl0o6WdLXbNtebQjZVfoahnPFD6G4vkR5jJ279s35FR67oPT1jkXPT2h+4KjX+ZJ227a91ODgRrhfzhpSlT7PeXJmx6x2vZ9nSzpey8xSWsZJpa8HVvk6P9WjTspKY8KOl+czllqp7pS02TiLRnqfn5GzcOWdK2nNqiNf68UY8045oe77kn53laFOauy5strf+wWq+PnW9Rz0UU314jLGdEn6r9Jx3lRFqJPCce1w+VIvS6j0OVfyfkGfL37XiTuGu5rFdMN0rtQsLNeXSAQ7Y8wJxphTFt19k5wZSm82xnR6nrtVTrfQT23bftzzfLfF6i8WHftFkk7U6lu0fFX6x/RGSccZZ0Vwr3fImR2zYKmSJerF66izlIwx6yrcl5LTJSctvdBk3flVJ5U+Y8nfyBnsuvgzVjxX5Awib1OMzhVjzLskfUROl9LvLbcMUUjOlRX/3peWZDmlNM7Ua8U/32rqOiA114sn1D1D0h/Ytr3kWo3GmLZKy90YY06W00twSPPdk0Hyo14qnfetmt/h52b3/tKxfibpMmPM0z3PT8lZDHxczrjFIPnxO+S+pkfS78sZJlKxKzVC58qqhPn6EtSWYmfImUEjOWM4JOnFZn7tnG/bC7c1+aGc9XHK3Va2bQ8bY/5SztowPysNZm2S9KdyAuvbPa+Xbdu3GmO+KulVxpib5fxybZf055IelvQx3z5g9f5W0uWSvly6KDwup55eKGeLJ3vR84+oF5cxpk/S/9DSs5Rc3zfG7Jf0G0l75SyU+Eo53ZPXKfiLkB918h5jzPlyLjy75QxGvULS8+T87N+/6BhfkLPW0J+W/rH7sZw1vN4mZ8eF//Drw9Wg5noxxrxVzuKYB+VcXF5mjPG+ZmLRQqSBnyur/L0/V87P/ItyBne7VvvzXW1dN5xP9XKrnKUaviWpaIx57aK3uc9zXT5J0n8aY74l6VFJE3JmOb5Rznih1y6xAkFD+VQv9xtjfiJn4feDcrZbe0Pp68cqTAp8u5xz6hZjzP+VM0bqNXIC81uWWDuyYXyqE9dr5YwD++wyvRiROFckyRjzOs1vlbZNkmWMeY/7uG3bH/Q8PbTXl6B2njhbzhIJXi8t/Sc5/c5H3a/Otu1rjDFDcnaN+KgW7ndXaUuUN8hp+vxDObMhhyRdK2ev2KXWd2sY27afKAWQD8lJ9+6ecX+spXeMWMrr5fyyHq15/BtyTqK3yZmCPSWnjt4s6XMBdzn6VSe3STpVzjInfXLWM9whZ1mPj9qLtpKzbTtvjLlSzk4kr5D0Kjl7/V0lZ6+/QLcTk3yrF3em8AZV3kljtxYucRGKc6WK3/vFr1/Vz9fn38u6qbVe5IQ6yVnA9sUVHv97zV+XD8gJgpfKqb9WOS0vN8sJO3dV+zn85kO9fFXO53y2nG2fxuX8cfMO27ZvqPB+dxljLpTTkv1Xmt8r9mW2bX/Dh49UMx/qxPUmOUu//Mcyz4nMuSInbF666D5vVvmgjiIM1xerWAz0320AAAD4JBJj7AAAAHB0BDsAAICYINgBAADEBMEOAAAgJgh2AAAAMUGwAwAAiAmCHQAAQEwQ7AAAAGKCYAcAABATBDsAAICYINgBAADEBMEOAAAgJgh2AAAAMUGwAwAAiAmCHQAAQEz8f7USYofXZSnjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "last_state_dict = copy.deepcopy(net.model.state_dict())\n",
    "\n",
    "Nsamples = 10\n",
    "\n",
    "for idx, iteration in enumerate(savemodel_its):\n",
    "    \n",
    "    net.model.load_state_dict(save_dicts[idx])\n",
    "    weight_vector = net.get_weight_samples(Nsamples=Nsamples)\n",
    "    fig = plt.figure(dpi=120)\n",
    "    ax = fig.add_subplot(111)\n",
    "    symlim = 1\n",
    "    lim_idxs = np.where(np.logical_and(weight_vector>=symlim, weight_vector<=symlim))\n",
    "    sns.distplot(weight_vector, 350, norm_hist=False, label='it %d' % (iteration), ax=ax)\n",
    "    ax.legend()\n",
    "\n",
    "    ax.set_xlim((-symlim, symlim))\n",
    "\n",
    "\n",
    "net.model.load_state_dict(last_state_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n"
     ]
    }
   ],
   "source": [
    "print('a')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weight SNR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2395210,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'SNR (dB) density: Total parameters: 2395210')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "name = 'Bayes_By_Backprop'\n",
    "\n",
    "# mkdir('snr_weights')\n",
    "SNR_vector = net.get_weight_SNR()\n",
    "np.save(results_dir+'/snr_vector_'+name+'.npy', SNR_vector)\n",
    "\n",
    "print(SNR_vector.shape)\n",
    "\n",
    "fig = plt.figure(dpi=100)\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "sns.distplot(10*np.log10(SNR_vector), norm_hist=False, label=name, ax=ax)\n",
    "# ax.set_xlim((-2, 2))\n",
    "# ax.hist(weight_vector, bins=70, density=True);\n",
    "\n",
    "ax.set_ylabel('Density')\n",
    "ax.legend()\n",
    "plt.title('SNR (dB) density: Total parameters: %d' % (len(SNR_vector)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2395210,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'SNR (dB) CDF: Total parameters: 23928000')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "name = 'Bayes_By_Backprop'\n",
    "\n",
    "# mkdir('snr_weights')\n",
    "# SNR_vector = net.get_weight_SNR()\n",
    "# np.save('weight_samples_'+name+'.npy', weight_vector)\n",
    "\n",
    "print(SNR_vector.shape)\n",
    "\n",
    "fig = plt.figure(dpi=100)\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "sns.distplot(10*np.log10(SNR_vector), norm_hist=False, label=name, ax=ax, hist_kws=dict(cumulative=True), kde_kws=dict(cumulative=False))\n",
    "# ax.set_xlim((-2, 2))\n",
    "# ax.hist(weight_vector, bins=70, density=True);\n",
    "\n",
    "ax.set_ylabel('Cumulative Density')\n",
    "ax.legend()\n",
    "plt.title('SNR (dB) CDF: Total parameters: %d' % (len(weight_vector)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### evaluate pruning performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# net.load(models_dir+'/theta_last.dat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0.1, 0.2, 0.4, 0.6, 0.8, 0.9, 0.99, 0.995]\n",
      "-inf\n",
      "params: 2395210\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:7: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.from_iter(generator)) or the python sum builtin instead.\n",
      "  import sys\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Delete proportion: 0.000000\n",
      "\u001b[34m    Jtest = 0.109391, err = 0.026600\n",
      "\u001b[0m\n",
      "-23.970589 dB\n",
      "params: 2155689\n",
      "Delete proportion: 0.100000\n",
      "\u001b[34m    Jtest = 0.108832, err = 0.027400\n",
      "\u001b[0m\n",
      "-21.093139 dB\n",
      "params: 1916168\n",
      "Delete proportion: 0.200000\n",
      "\u001b[34m    Jtest = 0.106589, err = 0.026000\n",
      "\u001b[0m\n",
      "-18.124451 dB\n",
      "params: 1437126\n",
      "Delete proportion: 0.400000\n",
      "\u001b[34m    Jtest = 0.104356, err = 0.026600\n",
      "\u001b[0m\n",
      "-15.006608 dB\n",
      "params: 958084\n",
      "Delete proportion: 0.600000\n",
      "\u001b[34m    Jtest = 0.100686, err = 0.025800\n",
      "\u001b[0m\n",
      "-10.770768 dB\n",
      "params: 479042\n",
      "Delete proportion: 0.800000\n",
      "\u001b[34m    Jtest = 0.095435, err = 0.026300\n",
      "\u001b[0m\n",
      "-9.388739 dB\n",
      "params: 239521\n",
      "Delete proportion: 0.900000\n",
      "\u001b[34m    Jtest = 0.094517, err = 0.026600\n",
      "\u001b[0m\n",
      "-5.429064 dB\n",
      "params: 23953\n",
      "Delete proportion: 0.990000\n",
      "\u001b[34m    Jtest = 0.095008, err = 0.027500\n",
      "\u001b[0m\n",
      "-2.286946 dB\n",
      "params: 11977\n",
      "Delete proportion: 0.995000\n",
      "\u001b[34m    Jtest = 0.103515, err = 0.030300\n",
      "\u001b[0m\n"
     ]
    },
    {
     "data": {
      "image/png": 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/81OzbERERMQ1ypCIiIiI67SXjYiIiLiuURkSERERcZvWIRERERHXnRlD4nJFWqGAREREJA74m2bZxGZEooBEREQkDmiWjYiIiLhOs2xERETEdcqQiIiIiOuUIRERERHXBeMRzbIRERER94QyJJ4YjUgUkIiIiMSB0BgSn8aQiIiIiFs0hkRERERcp1k2IiIi4rozY0hcrkgrYrRaIiIiEk1nZtkoQyIiIiIuUZeNiIiIuC7UZeOL0UGtCZEWMMaMA34NXAbUAc8A37HWnuhA2XuAB4GRwA7gx9bahc3u/QAwDxgBVAJFwPesteXN7nU58GNgKuAH1gSvWxXpYxIREentetU6JMaYdGApkArcBnwL+CzwWAfKzgeeAF4GrgeWAM8ZY64Ju+wa4ArgUeBG4HvB31caY/qH3Wsi8BZwErgbuBfoBxQZY0ZG8phERETiQax32USaIbkfyACmWmsrAYwxDcBTxpifWmtL2ij7ELDQWvtg8Pd3jDHjgZ8AbwePPQs8Yq0NhAoZYzYA63ECoCeDh28N/vxMKDNjjFkFlOMEO/8V4eMSERHp1fx+56c3RgdrRFqtG4CiUDAS9CJQHzzXImPMKGA8TvdOuKeBGcaYQQDW2srwYCRoI9AIDA07lgicwsmQhNTidN3EZugnIiLiokCMZ0giDUgmAGdlQay19cDO4Lm2ytG8LLAFJ4AY30bZ2YCvWdmngsd+ZowZZIzJAX4FHAIWnnsLERGR+NbYywa1ZgA1LRyvBjLbKUcLZauDP1ssa4xJBB4GLPBa6Li1dpsxZh7wCvDt4OH9wNXW2kNtPYD2JCREL5fl83nP+ildpzaNPrVp9KlNo09t2nWhDElCgo+EBG/MtWnEs2yA5l0q4GQ5WjreXllPK8dDfgtMAi631jaEDgZn47yIMzD2CZwunG8AbxhjLrHWftKBupzD6/WQkZHSmaJtSktLjvo9453aNPrUptGnNo0+tWkXBLtq0gckn/VZFyttGmlAUs2ZbEe4dM7tjmlejmDZg83KhZ9vYoz5IfBlnIGrq5ud/tfgfT4XGnNijFkK7MKZ+fP1th9Gy/z+ALW1xztTtEU+n5e0tGRqa0/Q2OiP2n3jmdo0+tSm0ac2jT61adc1NDjtduzYSaqr66LapmlpyV3OtEQakJTQbKyIMSYJGA38sZ1yBMtuDTuej5MdCT+GMearwI+A+621i1q4Xz7wYfgAWGvtSWOMDdal00L/w6KpsdHfLfeNZ2rT6FObRp/aNPrUpp0XmvYbCATOasNYadNIw5k3gHnGmIFhx24FkoLnWmStLcUJOu5sdmoBsCp81o4x5i7gN8APrLWPtnLLPcA0Y4wnrFw/nEBld4cfjYiISJwILYwWq7NsIs2Q/B5nJdVXjDEPAVnAL4GnwtcgMcY8DtxjrQ2//w9wFkLbCSwGbsFZCO26sHJXAP8DfAAsNsbMCit/yFq7M/jfvwMWAc8YY54A+gDfxOkS+n2Ej0lERKTXC82y8cboLJuIMiTW2hpgLs6S8S/hBCPPAPc1u9QX/BdediHwRWA+ziqr1wB3WmvfDrvsKpwBqlcAK5v9+37YvV7FWShtJM5iaqHuonnW2g2RPCYREZF4EOvrkEQ8y8Zauw24tp1r7sVZzr358Sc5s9pqS+V+hDN2pCP1eAknKBIREZF2NC0d3xsyJCIiItIzNS0dH5vxiAISERGReBDrm+spIBEREYkD/t40qFVERER6JgUkIiIi4qpAINC0R4u6bERERMQVofEjoAyJiIiIuMQftjJ8jMYjCkhERER6O2VIRERExHWhAa2gMSQiIiLiEmVIRERExHXKkIiIiIjrwuIRYjQeUUAiIiLS24UyJB4PeGI0IlFAIiIi0suFAhJfjI4fAQUkIiIivV6sb6wHCkhERER6vVBA4lGGRERERNzStLGeMiQiIiLiltAsmxhOkCggERER6e0aG5zNbPz+AFv3VJ+1LkmsSHC7AiIiItJ9im0F//uWBeDEqUb+/Zm1ZKQm8blrDdfMHuVy7c5QQCIiItJLFdsKHnl50znHq4/W85sXNtA/JYkJeQNcqNm51GUjIiLSC/n9AZ4u2t7mNY+9silmum8UkIiIiPRC2/bWUH20vs1rKmtOYD+pPk81apsCEhERkV6opq7tYKTpumOnurkmHaMxJILfH2Db3hpq6upJT0liXF56zG5PLSIiHZOektSx6/r36eaadIwCkjhXbCt4umj7WWm9jNQk7i4cS4HJcrFmIiLSFePy0knv36fNDMig9GTM8IyYGEeiLps4Fhp93byPsfpoPY+8vIliW+FSzUREpKu8Xg+DBvRt85r7bpkUMxlxBSRxqiOjr58p2h4TUbOIiETuwy3l7NhXiwdI7Zd41rnM1CQemD+ZOZOHulO5FqjLJk51ZPR11dF6tu2tYfyIjPNUKxERiYaq2pP8+a1tANx8yUg+dcmoc8YK9unjc7mWZ1NAEqc6PPq6g9eJiEhs8AcCPP56CcfrGxg1JI2b5ozE6/XE/JdLddnEqQ6Pvu7gdSIiEhuKVpdRsqeaPgle7rs5nwRfz/io7xm1lKgbl5dOcjvpusxUJ60nIiI9w75Dx3jh3Z0A3Dl3DDmZ/VyuUccpIIlTH2+t4MSpxjavWVA4NmZGX4uISNsaGv089uoWGhr9XHjBQK6cNsztKkVEAUkc2ra3hsdf3wLA5NEDyUg9u1vG64G/+/RErUMiItKD/OWDUj6pOEb/5ES+eMN4PJ6e9YVSg1rjzIHDdfzmxQ00NAa4aNxgvvrpSYATpByqOcEzS7Zz8lQjDQ2a7isi0lNs21vDmx/uAeCe6wzp/Xve+D9lSOJIbd0p/t/z66k72cAFQ9O47+Z8vF5P0+jry6YM5cbZIwBYtGK31iAREekBTtQ38IfXthAALpmU02Oz2wpI4kT96UZ+9cIGKo+cZHB6X75+22SSEs8d1Dr3olxS+iZwsOo4H5UcdKGmIiISiWeWbKfyyEkGpvVlQeE4t6vTaQpI4oDfH+DRRZspPVBLSt8Evnn7FNJSWt5MKTkpgWtnDAfg1eXKkoiIxLI12w6xbMMBPMBXbppAv749dySGApI48NzSHazdXkmCz8MDt01myMCUNq+fV+BkScqrjrNqq7IkIiKx6Mixep54cysA180cjhke2wuftUcBSS+3ePVeFq/eC8BXbsrv0LoiyUkJXHNxHqAsiYhILAoEAvzpza0cO3GavKz+fPqyC9yuUpcpIOnF1mw7xLPBDfTmXzmaGROyO1x2XkEeKX0TOHD4OB9v1a6/IiKx5L31+9mw8zAJPg/33ZxPYkLP/zjv+Y9AWrRrfy2PLtpMALhy6lCunzk8ovL9+iZwdShLohk3IiIx42DVcZ5d4nzZvO2K0eQO7u9yjaJDAUkvVFFzgl+9sJ5TDc5qfZ+9ZlynFsgpLMijX1IC+yvrWG2VJRERcVuj389jr23h1Gk/44enN31x7A0UkPQyx06c5uHn13P0+GmGZ/fnb2+ZiM/buf/N/fo2G0sSUJZERMRNr6/cw679tSQnJfDlG/Px9rDVWNuigKQXOd3g57cvbqC86jiZaUl8Y/4UkpO6NgWscHouyUkJ7Kuso9geilJNRUQkUqUHalm0bDcAn7tmHAMH9HW3QlGmgKSX8AcC/PGNEraVHSE5ycc3b59yzh41ndGvbyJXT88FYNGyUmVJRERcUH+6kcde3YI/EODi8VnMyu/4JIWeQgFJL/Hy+7v4aMtBfF4PX7v1wqgOcrrm4rymLMkaZUlERM67he/soLzqOAP69+Hz15oet3FeRygg6QXeXbeP11c6myrde/148kdmRvX+Z2VJlitLIiJyPm3cdZila/YB8OUbJ9A/OdHlGnWPiAcYGGPGAb8GLgPqgGeA71hrT3Sg7D3Ag8BIYAfwY2vtwmb3fgCYB4wAKoEi4HvW2vKw654A7mnlzzxorf15pI+rp9qw8zB/fmsbAJ+6ZCSXXDikW/7O1RfnsXj1XsoOOVmS6eN75uZNIiI9ybETp/njGyWAs4r2pFEDXa5R94koQ2KMSQeWAqnAbcC3gM8Cj3Wg7HzgCeBl4HpgCfCcMeaasMuuAa4AHgVuBL4X/H2lMSa8D+IhYHazfw8Hz70ZyWPqyT45eJT/emUT/kCAOZNyuOXSUd32t1L6JlJY4My4WaQZNyIi3S4QCPA/f93KkWOnGDKwH/OvHO12lbpVpBmS+4EMYKq1thLAGNMAPGWM+am1tqSNsg8BC621DwZ/f8cYMx74CfB28NizwCPW2qZPO2PMBmA9TgD0JIC1diewM/zmxpifA1ustesjfEw9UlXtSR5euJ76U41MGJHBvdeP7/Y+xTNZkmOs3VZJgRncrX9PRCSerdxczmp7CJ/XWY21pR3ae5NIx5DcABSFgpGgF4H64LkWGWNGAeNxunfCPQ3MMMYMArDWVoYHI0EbgUZgaBv3H4bThfRUBx9Hj3b8ZAMPL1xPzbFTDBuUwtdunUSCr/uHA/VPTqRQY0lERLpd5ZETPLU42B1/6ShG5qS5XKPuF+mn2ATgrCyItbYeJ1sxoZ1yNC8LbAE8OMFKa2YDvhbKhluA81iaBzy9TkOjn//6y0bKDtUxIKUP37h9Mv36nr8BTtdcPJy+fXzsrTjGuu2V7RcQEZGI+P0BHn+thBP1jYwelsYNsyLb+qOnirTLJgOoaeF4NdDW1I7QnsjNy1YHf7ZY1hiTiDM2xAKvtXH/u4GV1trSNq7pkIQoblDkC2YtfFHKXji7O1o2764mKdHHP941lZyBKVG5d0elpyZx9cV5vLp8N4uWl3LxhKzzOv0s2m0qatPuoDaNvnhq0zdW7sHurSEp0cff3jKJpD5dW+CyNbHWpp15lC3l6T2tHG+vrKeV4yG/BSYBl1trG1q6IDgOZRrO7Jwu8Xo9ZGRE/wM+LS05Kvd5drHlg/UH8HrgO/dczLQIdu+NpruunUDR6r18cvAY2/YfZdak7pnZ05ZotamcoTaNPrVp9PX2Ni3df4QX3nWGSN736UmMH939Y/VipU0jDUiqOZPtCJdO210qoUxIBnCwWbnw802MMT8Evgx8xlq7uo17fxZoAJ5v45oO8fsD1NYe7+ptmvh8XtLSkqmtPUFjo79L91q+4QBP/XUrAF+4bjyjc/pTXV0XjWp2SmFBHq+u2M2f3yxh3NDU85YliWabikNtGn1q0+iLhzY93eDnF/+7moZGP9PGDuLicYO69X0+mm2alpbc5UxLpAFJCc3GihhjkoDRwB/bKUew7Naw4/k42ZHwYxhjvgr8CLjfWruonTotwBloG5XtaBsaov9Eb2z0d+m+JXuq+cNrWwC4buZwLp8ytFvqGYnC6bm8/fFe9pQfpXjrIaaOHXRe/35X21TOpTaNPrVp9PXmNl24dAd7K46R2i+RL1w3nsbGAB3rfOiaWGnTSMOZN4B5xpjwlVluBZKC51oUHNuxFbiz2akFwKrwWTvGmLuA3wA/sNY+2lZljDEzcYKhpyN5ED3Jvso6fvvSRhr9zv4FsTIPPbVfH+YWDAPglWWlBDTjRkSk07buqeatVZ8AcO914xmQ0sflGp1/kWZIfo8zVuMVY8xDQBbwS+Cp8DVIjDGPA/dYa8Pv/wOchdB2AouBW3AWQrsurNwVwP8AHwCLjTGzwsofCq4/Eu5u4ATOYmu9zpFj9Tz8/HpO1DcwJncAX7lpQkxtNX3tjOEsLd7HnoNHWb/zMFPHnN8siYhIb3D8ZAOPv76FAHDZ5CFMGxefazxFlCGx1tYAc3GWjH8JJxh5Briv2aW+4L/wsguBLwLzgbdwgpE7rbVvh112FZBIcHXWZv++H34/Y4wPuAN41Vp7LJLH0ROcPNXAwy9s4HDtSbIzkvn6bZNJTIitRXHS+vVh7kVOlmSRsiQiIp3ydNE2DtfWMzi9L3fNG+t2dVzj0YdIk12Njf5RVVXRG0CUkOAlIyOF6uq6iPrnGv1+fvviRtbvPEz/5ET++QsFZGX0i1q9oqn2+Cn+73+t4NRpP9+YP5kp3Zwl6WybSuvUptGnNo2+3tqmq7dW8Lu/bMLjge989iLG5qa3XyhKotmmmZkp+HzeUuCCzt4jNiYfS5NAIMDTRdukF/OYAAAgAElEQVRZv/MwiQlevjF/cswGIxDKkpxZvVUBrohIx1QfrefJ4OzJG2aNOK/BSCxSQBJj3lq1l3fW7MMD3HdTPqOHDXC7Su26bsZw+iR6KT1wlI27qtyujohIzAsEAvzpjRLqTjYwIju1WzdH7SkUkMSQ1VsreP6dHQDcMXcM08dnuVyjjklL6cNV0zTjRkSko95Zu49NpVUkJnj5ys3552U/slinFogRO8qO8Oirzloj8y7K5ZqL81yuUWSumzmCPgleSg/UsqlUWRIRkdYcOFzH80udL5/zrxzNsEHndwuQWKWAJAYcrDrOr1/cQEOjn6ljBrGgcOx53R8mGgak9OHKaZpxIyLSloZGP4+9uoVTDX7yR2YwryDX7SrFDAUkLjt6/BT/b+F6jp04zcicVO7/1ES83p4VjIRcP3M4iQledu6vZbOyJCIi53htxW52lx8lpW8CX74xP6bWlnKbAhIXnTrdyK9f3EBF9QkGDejLN26fQlKf2FprJBID+iedGUuiGTciImfZue8Ir63YA8DnrzVkpCa5XKPYooDEJf5AgD+8XsLOfbX0S0rgm7dP6RVLBTdlSfbVsmX3OXsmiojEpZOnGnjstS34AwFm5Wczw6Xd2mOZAhKXvPDOTlZvrcDn9fDAbRcytJcMahrQP4krpg4FNONGRCTk+aU7qKg+QUZqEp+9Zpzb1YlJCkhcsHRNGX8NbqL0pRsnYIZnuFyj6Lph1ggSE7zs2HeELXuUJRGR+LZ+RyXvrtsPwFdunEBK30SXaxSbFJCcZ+u2V/LU4m0A3Hr5BcyemONyjaIvvX8SV0xxsiSacSMi8az2+Cn+9KazGus1F+cxYWSmyzWKXQpIzqPSA7X896JNBAJw+ZQh3DR7hNtV6jbXzxpBgs/L9rIjlChLIiJxKBAI8OSbW6mtO8WwQSncdkWnt3mJCwluV6C38vsDlOyu4nRpNYmeAANS+vCrFzZw6rSfSaMy+dw1psetNRKJjFRnLMmS4jIWLStlwoiMXv14e6rmz9PRQwf02GnnIrHA7w+wbW8NNXX1fHLwKGu3V+Lzerjv5vyY27E91igg6QbFtoKni7ZTfbS+6ZjX68HvD5A7uD9/9+lJcbFM8A2zRvDeun1sKzvC1k9qmDCid42V6elaep5mpCZxd+FYCkzP2LZAJJa09JoCmDEhi+HZqS7Vqufo/Z+K51mxreCRlzed84T0+51xFPMuGkZyUnzEgRmpSVweNpZEYkdrz9Pqo/U88vImim2FSzUT6Zlae00BrNx8UK+pDlBAEkV+f4Cni7a3ec2rK3Y3BSfx4IZZI0jwebB7a9iqsSQxoSPP02eKtsfV81SkK/Saig4FJFG0bW9Ni9FxuKqj9WzbW3OeauS+zLS+XBbKkixXliQW6HkqEl16TUWHApIoqqlr+wkZ6XW9xY2zRuDzetj6SQ32E2VJ3FZ9TM9TkWjaW3GsQ9fpNdU2BSRRlJ7SsX0JOnpdb5GZ1rdpLMkrGkvimuMnG1j88V4WBrc9b09qshZvEmlNIBCgZE81v3lxA88sabu7JiTe3vsjFR+jK8+TcXnpZKQmtZm6y0xNYlxe+nmsVWy4YdYI3l+/n62f1LBtb01ctoFbDhyuY2nxPpZtOkD9qUYAPEB7vdlPLd7GXfPGMXn0wG6vo0hPUX+6kY+2HKRo9V7KDtU1HU/weWhobP1VFa/v/ZFQQBJFXq+HuwvH8sjLm1q9ZkHh2Lhc52HggL5cNnkI767bzyvLSvmnBdPcrlKv5g8E2FxaxeLVe9m0q6rp+NBBKRQW5NK3j49HX93Savm+fXyUV53g4YXrufCCgdw1bwxDBvaO/ZZEOqOq9iRL1pTx/rr91J1sAKBPopdLJg1hbkEu5Yfr9N7fRQpIoqzAZPG1WyedMxc9MzWJBXG+vsMNs0fwwYYDlOypVpakm5yob2DFpnKKiss4WHUccLIhU8YMYt70XPLDFqhLTPC2+jydMCKDRct3s6S4jI27DrNldxVzL8rlU5eO1D4cEjcCgQDby45QtHova7ZV4g9ugzFoQF/mXpTLZVOGNL0ehg1K0Xt/F3m0z0iTXY2N/lFVVXXtX9kBfn+AnfuPcDrg0QqYYZ7861beW7ef/JEZfOuuyLMkCQleMjJSqK6uo6HB3w017Jkqqo+zpHgfyzbu50S90y2TnOTj0guHMq9gGFkZ/Vos197ztLzqOM8v3cG6HZUA9E9O5NbLRnH51KH4vBqC1ho9T6PvfLbp6YZGPtpSQVHxXj45eGbA6vjh6Vw9PY8pYwa1+n4evlJreorTTROr7/3RbNPMzBR8Pm8p0On18RWQnBHVgAT0ptSSypoTPPjohzT6Azz4uYsYmxtZlkRtekYgEGDLnmqWrC5j/Y7KpjEh2Zn9KCzIZc6knA4twteRNt1Uephnl+xgf6Xz+hg2OIUF88aSr43CWqTnafSdjzatPlrPO2v38d66fRw9fhpwMomzJ+ZQWJBLblb/bvm7bom1gERdNnJeDUpP5pILh/D++v0sWlbKP3YiSxLv6k81snKz0y0TChAALrxgIIXTc5k4KhNvlPcNmjRqID/+Ugbvrt3PXz7Yxb5DdfzHs+uYNnYQd8wdQ3YrGRiRWBcIBNi1v5bFq/dSbA/RGFy8LDMtibkX5XL5lKH014yz80IBiZx3N80ewfKNB9i8u5od+44wZtgAt6vUI1TWnGDpmn28v34/x+udQXVJfXxcOmkIcwuGdfugU5/Xy7yCXGbmZ/PKslLeWbOPtdsr2bDzMFdfnMfNc0bGzbYI0vOdbvCzeqvTLVN64GjT8XG5Ayicnse0cYPULXme6d1DzrtB6cnMmZTDBxsOsGhZKf9w51S3qxSzAoEA9pMaiorLWLv9EKEe1qz0ZOYV5HLJhUPo1/f8voz7Jyfy2avHceW0YTy3ZDubSqv460efsGLjAT5zxWguvXBIzPaZixw5Vs+76/bzztp91NadAiDB52VWfjbzCnIZkaNN8NyigERcceOckazYVM6m0ip27jvCaGVJznLqdCMfbjlI0eoyyg6dGVQ3cWQG86bnMfmCga5/6A8blMLf3zGFDTsP8+zSHRysOs4Tb25laXEZCwrHYoZrd2eJHaUHailavZdVJRVN3TLp/ftw1UW5XDF1KGn9+rhcQ1FAIq7ISk9m9qQclm04wCvLS/mHO5QlAWetg6VrnEF14WsdzJk0hHkFuQwbFFtrgXg8HqaMGcTEUZksLS7jleW7+aTiGP/29Fqmm8HccdUYBqUnu11NiVMNjX6K7SGKiveyc19t0/HRw9IoLMijwAwmwadumVihgERcc9OckazYWM6mXVXs3H+E0UPjM0vStNZBcRlr7KE21zqIVQk+L9fMGM6sSTn85YNS3lu3j9X2EOt2HObaGXncOHsEffvo7UbOj9rjp3hv3X7eWVNGzTGnW8bn9TBjQjaF03MZNSTN5RpKS/QOIa7JCo4lWbbxAIuW7ebv75jidpXOq9MNjawqqWDx6nPXOiicnsfUNtY6iFVp/frwhWsNV00bxrNLtlOyp5rXV+5h2cYDzL9iNLMn5UR9BpBIyCcHj1K0uowPtxykodGZxpqW0oerpg3jyqlDGdBfe8nEMgUk4qqb5oxgxaZyNu46zK79tVwwtPd/c2l9rYNsCgvyesVaB3lZ/fnWXVNZu72S55Zu51DNSR5/vYSla8pYMG8cY3LjMxsmneP3ByjZXcXp0upzFvBr9PtZu62SotV72VZ2pKnMqCGpFE7P4+LxWeqW6SEUkIirsjL6MXtiNss3lbNoeSnfvL33Zkl27nO6ZVZvrYiLtQ48Hg8XjRvMhRcMpGj1Xl5dsZvSA0f51z8XMzM/m9uvHE1mWl+3qykxrthWnLMce0ZqEp+5/AKO1J1i6Zoyqmqdcz6vh+njsygsyOWCoWlN2yRIz6CARFx30yUjWbn5IBt2Hqb0QG2v6t9taPTz8dYKilaXUXrgzKC6eFrrIDHBy/WzRjBnUg4vvb+LZRsO8NGWg6zddojrZ43gupnDSUr0uV1NiUHFtqLFDeuqj9bz+OslTb+n9kvkiqnDuGraMDJS1S3TUykgEddlZ/Rj1sRsVmwqZ9GyUr7RC7IkR+pO8e7afby7dh9HmtY68DAz3+mWice1Dgb0T+KLN0xg7kW5PF20je1lR3hlWSkfbNjP/CtHM3NCtr7RShO/P8DTRdvbvMbn9fD5aw2zJ2aTmKCgtqdTQCIx4eY5I1m5uZz1Ow+zu7yWkTk9M0virHVQxqqSg+eudTBlKGkpWutgRE4q3/nsRay2h3h+6Q4O157k0UVbWFq8jwWFY3tVhkw6b+ve6rO6aVrS6A+QlZ6sYKSXUEAiMSE7sx+z8nNYubmcRct28/X5k92uUoc1NPpZs+0Qi1drrYOO8ng8XDw+iymjB/LWqk94/cM97Nh3hIeeXM2cSTncdsVopd7jVNmhY6zcXM776/d36PqauraDFuk5FJBIzLj5kpF8uKWcdTsq2VN+NOa7NVpf6yCLwul5+qbfAX0Sfdx8ySgunTyUF97dycrN5azYVE6xPcSNs0dw7Yw8ffuNA9VH6/loy0FWbi5nb8Wx9guESU9R4NpbKCCRmJGT2Y+Z+dl8uPkgi5aX8sBtsZkl0VoH0ZeRmsR9N+czt2AYzxZtZ+f+Wl56fxfvr9/PHVeNocAM1viSXuZEfQPF9hArN5ezdU81wW2a8Hk9TB49kJn52Ty3ZDvVwWC/JZmpSYzLSz8/FZZup4BEYsrNc0Y6MzC2x1aWpGmtg+Iytu2taTo+MieVq6fnMX18FokJ6pbpqtFDB/Ddzxfw4ZaDvPDuTiqPnOR3f9nEuLx0FswbGzPPB+mchkY/m3ZV8eGWctZur+R0g7/p3NjcAcyemMP08VlNU+B9Xk+Ls2xCFhSO7XGLB0rrPIHQ9qGyq7HRP6qqqi5qN0xI8JKRkUJ1dR0NYS88adujizbz4ZaDTBs76Jwsyflu02MnTvPB+v0sXVPG4bC1DgrMYAqn5zG6F6x1EKvP0/pTjbz50R7e/OgTTjf48QCXTRnCrZePZkCMDw6O1TZ1QyAQYOf+WlZuLufjkgqOnTjddG7IwH7MnpjDrPzsVvc8amkdkszUJBYUjqXAZHV7/XuzaD5PMzNT8Pm8pcAFna5Pl2og0g1uCsuSfHLwKMOzz/+34rKKYxQVl/Hh5nJOBV+o/ZMTuXLaUK6alqsBl+dBUh8fn77sAi6bPJSF7+5gVUkF768/wKqSCm6+ZCSFBXnKSsWw8qrjfLi5nJWbyzlUc7LpeFpKH2blZzN7Yg7Ds/u3G9AXmCymjR3Mzv1HOB3wnLNSq/QeCkgk5gwdlMKM/Gw+2nKQRct3838+c+F5+bt+f4D1O5xumZI91U3Hh2f1p3B6HjPzszTA0gUDB/Tlb2+ZxLyCGp4u2s6e8qMsfGcn763dz51zxzB17KAen6XqLWrrTrGq5CArNx88ayHApEQfF40bzOxJ2UwYkRHxYoBer4cJIzOVderlFJBITLp5zkhWbTnImm2H2FtxjLxu3N/l+MnTfLDhAEuKy6g84nyT83o8XDRuEIXT8xibO0AfeDFgbG46379nOis2lvPiezupqDnBb17ayIQRGSwoHEvu4J6/B1BPVH+qkbXbD7Fy80E2l1Y17Vbt9XiYOCqT2ROzmTZ2MEl9FMxL2xSQSEwaOiiFiydksaqkgkXLS/nardHPkuyvrGNJcRnLNx3g1GnnG1dK34SmJagHDtA+K7HG6/Fw6eQhFJjBvPHhHt5atZeSPdX88I+ruHLqMD592ShS+8X2+JLeoNHvp2RPNSs3OV8a6k83Np0bNSSN2ROzmTEhWwsBSkQUkEjMunnOSD4uqaDYRi9L4g8E2LjzMEXFZWwurWo6PmxwCldPz2Nmfrb2VekBkpMSuO2K0Vw+ZSjPv7ODYnuId9bu46MtB/nUpaOYe9EwLUYXZYFAgE8OOouWfbTlYNOWCACD0/s6g1Mn5pCT2c/FWkpPpoBEYtawwf2ZPj6Lj7dW8OryUr7ahSzJifoGlm04wJI1ZVRUnwDAA0wd63TLjB+erm6ZHmhwejJfu/VCtu6p5pkl29lbcYxnl2zn3bX7uGveGCaPHuR2FXu8ypoTfBhctOzA4eNNx/snJ3LxhCxmT8zpFbPNxH0KSCSm3XzJSD7eWsFqe4iyQ8cYGeHqp+VVx1lSXMayjQeoP+WklfslJXD5lKFcddEwBrcy1VB6lvEjMvjhvRfz/ob9vPz+LsqrjvPwwg1MuiCTu+aOZeigFLer2KMcO3Ga1VsrWLm5nO1lR5qOJyZ4mTpmELMn5jDpgkxloSSqIg5IjDHjgF8DlwF1wDPAd6y1JzpQ9h7gQWAksAP4sbV2YbN7PwDMA0YAlUAR8D1rbXkL97sleL/JwEmgGLjTWlvV/FrpmXKDWZLVWytYtLxje9z4AwG2lFZRVFzGhp2Hm44PGdiPwul5zJmYowF2vZDX6+HKqcOYMT6b11bsZvHqvWzaVcUPd6/iqouGcculo0jpm+h2NWPW6YZG1u84zMrN5WzYebhpc0gPTsA3e2IOBWYwyUn6HivdI6JnljEmHVgK7AFuA7KAXwIDgc+1U3Y+8ATwc+Bt4NPAc8aYI9bat4OXXQNcATwKrANygR8BK40xF1prj4Xd717gv4H/wAlK+gNXAhpF1ct8as5IVm+tYPXWCt5fv4/0tH4trkVw8lQDKzaVs6S4rCm17AEmjx5I4fQ88kdmKK0cB/r1TeCOuWO4YupQnlu6g3U7KilaXcbKTeV8+rILuHLa0IinnfZW/kCA7XtrnEXLth7iRH1D07m8rP7MnpjDzPxsrbsj50Wkoe79QAYw1VpbCWCMaQCeMsb81Fpb0kbZh4CF1toHg7+/Y4wZD/wEJ0ABeBZ4xFrbtHysMWYDsB4nAHoyeGwgTpbm69baR8P+xqsRPh7pAXKz+nPB0DR27a/lD6+eeYplpCZxd+FY8rJTWVpcxgcbDjS9ofbt4+PSyUOYV5BLdoYG2cWj7Mx+fH3+ZDaXVvHsku3sq6zjqcXbnPElhWOZODLT7Sq6JrSj7kdbDlJVe2YF1IzUJGZNzGZ2fg653TjVXqQlkQYkNwBFoWAk6EXgj8FzLQYkxphRwHjgu81OPQ38yRgzyFpb2ey+IRuBRmBo2LE7cL78PhFh/aUHKrYV7Npfe87x6qP15+xzkZ3Zj8KCXOZMylFqWQCYOCqTH33pYt5du5+/fLCLfZV1/Oez65g6ZhB3zh1DdpzMCmltR93kpASmm8HMnpjDuOHpeJVFFJdE+o49ASf4aGKtrTfG7Ayea6scnBuwbCHYRQksa6XsbMDXrOwswAL3GmP+GRgCbAD+yVq7tAOPQ3oIvz/A00Xb271u4qgMrp4+nEkXZOoNVc7h83qZV5DLzPxsFi0rZemafazbUcnGXYe5enoeN80ZSb++vS+AbW9H3dkTc5gyZqBWIJaYEOkrMAOoaeF4NdBW/jMj+LN52dD63C2WNcYkAg/jBB+vhZ3KAQzO+JJvAxXA3wNvGGPyrbW72qhLmxKiuDeGLzgC3aeR6J1WsrvqrE21WvOpS0YxIY5T8F0RT8/T9NQkvnD9eOZdnMczi7exYedh/rrqE5ZvOsD8K51xJ9HYI8XNNm1o9LNx12FWbCxnzbZDZ+2oOy4vnTmTcpiRn920o25PEU/P0/Ml1tq0M18JWtoe2NPK8fbKelo5HvJbYBJwubW2Iey4F2cQ653W2jcAjDHvA6XAt4CvdqAu5/B6PWRkRH96YFqappZ21unS6vYvAk4Huuf/XTyJp+dpRkYKPx2bxeqSg/zhlU3sO3SMP71Rwnvr9vOVT0/iwiitX3K+2jQQCGD3VPNO8V4+WLefo8fPLFqWm9WfKwtyuWJaLjkDe/5rJJ6ep+dLrLRppAFJNWeyHeHSaWX8SFg5gmUPNisXfr6JMeaHwJeBz1hrVzc7HZrW+07ogLX2hDHmQ2BiG/Vok98foLb2ePsXdpDP5yUtLZna2hM0NmozqM5I9HQkznWuq66u6+ba9E7x/DwdndOfh74yg6LVZfzlg13s2n+E7/5uORePz+KueWMZnNG5N+rz1aYHDtexclM5KzaWU1FzZuWFASl9mDUphzmTchiZk9o0u6wnv0bi+XnaXaLZpmlpyV3OtEQakJTQbKyIMSYJGE2zsSUtlCNYdmvY8Xyc7Ej4MYwxX8XpjrnfWruolfu1lqnpUqt2xy6SjY1+7U7ZSaOHDiAjNanNbpvM1CRGDx2gNu6ieH6eFhbkMnNCFn/5oJR31+3j460VrN1eybUz8rhh1ohOD5DujjaNdEfdxsYAHUtg9wzx/DztLrHSppG+yt4Avm+MGWitDa04dSuQFDzXImttqTFmK3An8HLYqQXAqvDZNcaYu4DfAD9oNqU33GvAD3EWUHstWK4fzgDYpyN8TBLDvF4PdxeOPWc2TbgFhWOj0u8v8S21Xx8+f63hqmnDeGbJdkr2VPP6yj0s23CA264YzZwLc1wbMK0ddSUeeAKBjkfOwYXRNgG7cdYVCS2M9pa19nNh1z0O3GOtTQg7djvwHPAzYDFwC/AN4LrQwmjGmCuC51YA32n25w9Za3eG3e8vOLNtvsOZQa2zAWOt3dfhB3XGrsZG/6iqquilNBMSvGRkpFBdXRcT0WdPVmwreLpo+1mZkszUJBYUjqXAZLlYs55Pz9NzBQIB1m2v5LmlO5q6QkbmpLKgcCxjc9PbKR2dNm17R91UZk3MYcaEbAbEyY66ep5GXzTbNDMzBZ/PWwpc0On6RHKxtbbGGDMXJ4PxEnAcZ+n4bze71Bf8F152YTCL8V2cgac7cAalvh122VVAIs5qrSub3fNJ4N6w3z8H/BvwCyAVWAXM62QwIjGuwGQxbexgdu4/wumAp8WVWkWixePxMG3cYCZdMJCi4r28unw3u8uP8rM/r2HGhCxuv3IMAwf0jfrfbW9H3Vn5OcyamM2QXjA4VaS5iDIkvZwyJD2A2jT61KbtO1J3ipfe28myDQcIAH0SvFw3czjXzxzRYjdJpG2qHXXbp+dp9PXoDImISDwakNKHL94wgbkX5fLMku1s21vDouW7+WDDAeZfOZpZ+dlNwYLfH6BkdxWnS6vbzOSFdtT9cHM527SjrogCEhGRjhqRk8q3755GsT3Ec0t3cLj2JI+9uoWlxWUsKBxH9dGT54x1Cu25VGCy2t1Rd9bEbArGZfXKVWNF2qMumzPUZdMDqE2jT23aOadON/LWx3t5Y+WeswactiZ/RDql5cfO2lE3d3B/Zk/KZuaEbDLToj8mpTfR8zT61GUjItIL9En0cfOckVx64RBeeHcHKzcfbPP6LXucnTO0o65IyxSQiIh0QUZqEpdNHtpuQAJw19wxFF6cpw0gRVqg0VIiIl1UU9f+BpAAaf37KBgRaYUCEhGRLkpPSYrqdSLxSAGJiEgXjctLJyO17WAjMzWJcXntr/IqEq8UkIiIdFFoz6W2aM8lkbYpIBERiYICk8XXbp10TqYkMzWJr906SXsuibRDs2xERKJEey6JdJ4CEhGRKPJ6PUwYmalFvEQipC4bERERcZ0CEhEREXGdAhIRERFxnQISERERcZ0CEhEREXGdAhIRERFxnScQCLhdh1hxIhAI9PX7o9sePp+XxkZN+4smtWn0qU2jT20afWrT6ItWm3q9Hjwez0kgubP3UEByRg2QBBxwuyIiIiI9zBCgHuj0hk0KSERERMR1GkMiIiIirlNAIiIiIq5TQCIiIiKuU0AiIiIirlNAIiIiIq5TQCIiIiKuU0AiIiIirlNAIiIiIq5TQCIiIiKuU0AiIiIirlNAIiIiIq5LcLsCPZUxZhzwa+AyoA54BviOtfZEB8reAzwIjAR2AD+21i7svtr2DJ1pU2NMGvAPwPWAAU4DxcB3rbVrur3SMa4rz9Owe9wKvARsttZO6paK9iBdfO1nAv8C3ApkAJ8A/2mt/X331Tj2dbZNjTEpwPeB23E2d9sHPAX8zFpb362VjnHGmDHAt4BZwCRga0dfv8aYbwH/B8gBNgL/ZK19t5uq2kQZkk4wxqQDS4FU4Dac/+mfBR7rQNn5wBPAyzgfokuA54wx13RXfXuCLrTpcOB+oAi4E/gi4ANWGGMu6rYK9wBdeZ6G3SMZ+CVwsDvq2NN08bXfH3gPmA58A7gO+AXO8zVudfF5+l/AV4GHgRuBPwDfxWnXeDcRp012AFs6WigYjPwr8FvghmD5N40xF3ZHJcMpQ9I59+N8u5lqra0EMMY0AE8ZY35qrS1po+xDwEJr7YPB398xxowHfgK83Z2VjnGdbdNSYLS19njogDGmCNgFPIAToMSrrjxPQx7E+RZfivNBGu+60qbfBZKBGWHf/N/tzsr2EJ1qU2NMAk5m5N+ttb8JHn7HGDMC58vJ17u/6jHtVWvtKwDGmCfowOvXGJME/DPwsLX2P4LH3sPJknwPuKvbaosyJJ11A1AUevEEvQjUB8+1yBgzChiPk44M9zQwwxgzKNoV7UE61abW2rrwYCR47CRQAgztjor2IJ1q0xBjzGjgH9Ebe7iutOmXgMcj6S6LE51tUw/Ol+ojzY7XBM/FNWutvxPF5gADCPuMstY2As8BNxhjurVdFZB0zgScD7wmwf7KncFzbZWjeVmcdJoHJ1iJV51t03ME+5WnNb9fHOpqm/4K+B9r7fpuqFtP1ak2DX4ZyQaqjTGvGWPqjTGHjTGPBLvF4lmn2tRaexr4E/CAMWamMaa/MeYq4D6c7gaJXKi9tzY7vgWnS21Yd/5xddl0TgZOFN5cNZDZTjlaKFsd/NlW2d6us23akn8B+qE3pU63qTHmZpxvS+O6oV49WWfbNCf48xfAQpxv/vnAz4A+OB+i8aorr/2/A/4b+DDs2G+stT+JUt3iTQZQ30IWLxGcKjcAAAO9SURBVPwzqqy7/rgCks4LtHDM08rx9sp6Wjkeb7rSpgAYY+4Gvgl8zVq7I1oV68EiblNjTF+cQYI/bJZGF0dnnqehbHSJtfZLwf9eYoxJBH5hjPm+tbY8mpXsYTr72v85cBPwN4AFCoAfG2OqrbU/jG4V40Zr/y9aOxc1Ckg6p5oz2Y5w6bTdTRCKMjM4e9ZCerPz8aizbdrEGHM1Tgr3F9ba30Wxbj1VZ9v0m4AfeCY4AwKcb/He4O/HrbWnolrTnqOzbVoV/Lm02fGlOMHKBCBeA5JOtakxZhLOjJxbrLWLgoffN8b4gf8wxjxira2Iem17t2qgrzGmb3AsXsh5+YzSGJLOKaFZ32ZwdPJo2n5TCp1r3i+ajxN5Nu+3iyedbdPQtTNw1spYCHy7OyrYA3W2TccDY4BDOG9A1cCC4L2qcQZnxqvOtulOoKUgLvTNszMDEHuLzrZpfvDnumbH1+F82R4RrQrGkbY+o47irPPSbRSQdM4bwDxjzMCwY7cCScFzLbLWluIEHXc2O7UAWBXn6fFOtSmAMWZC8JrlwBettfHe9RXS2Tb9OXBVs39vAbuD/72o1ZK9X2df+6eAxcC8ZqfmAQ1EsE5EL9TZ5+me4M+CZsdD01t3R6V28WUFzqylps8oY4wPuAN4o7vfWz2BgN67IxVMW2/CecI/BGThLB71lrX2c2HXPQ7cY61NCDt2O84Uqp/hvEHdQnCRJGtt3K5D0tk2NcZkAauBROBzOKs8htRba9eelwcQg7ryPG3hXk8A0+N9pdb/384dqzQMRXEY/6CbD+AmOgiBOussDg6CiwjOgpsg6CC46OTo1M1ZUBBUUHTRQRAEBxen8wwuPoLDSbFYp4Z6C/1+EEIghTT09v5zc+9p2PYXgBfgAjgjnzqPgdOI2P2v7zBqGrT9FtmBzgBH5BySeeAQuIuIodbMGHVVVU3ws2x6mxxx2quPnyPis6qqJ2A6ImZ7PtctjHYAvANbwBpZP+djmNfsCMkAIuILWCI7vyuy8ZzTP1O+xa8qjHWJ+E1gnXzqXAY2xjmMQKN72gamyFUMj8Brz3Y93KsebU1+p/pbw7b/RlbObAO3wD7Qqfdja9B7WtfHWAVuyNe092Tn2an3426SfIV9CSyS/5Pd47n6nBb9c0lPyCJoO8ADudJuZdhhBBwhkSRJI8AREkmSVJyBRJIkFWcgkSRJxRlIJElScQYSSZJUnIFEkiQVZyCRJEnFGUgkSVJxBhJJklScgUSSJBVnIJEkScUZSCRJUnHfh3PEbEC6QKwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "delete_proportions = [0, 0.1, 0.2, 0.4, 0.6, 0.8, 0.9, 0.99, 0.995]# 0.9999, 0.99999, 0.999999, 0.9999999, 0.99999999] # \n",
    "# keep_proportions = 1 - delete_proportions\n",
    "\n",
    "\n",
    "batch_size = 256\n",
    "Nsamples_predict = 100\n",
    "# Nsamples_KLD = 20\n",
    "\n",
    "net.set_mode_train(False)\n",
    "\n",
    "if use_cuda:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=4)\n",
    "\n",
    "else:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=False,\n",
    "                                            num_workers=4)\n",
    "err_vec = np.zeros(len(delete_proportions))\n",
    "\n",
    "print(delete_proportions)\n",
    "\n",
    "for idx, p in enumerate(delete_proportions):\n",
    "    if p > 0:\n",
    "        min_snr = np.percentile(SNR_vector, p*100)\n",
    "        print('%f dB' % (10*np.log10(min_snr)))\n",
    "        og_state_dict, n_unmask = net.mask_model(Nsamples=0, thresh=min_snr)\n",
    "#         print(net.model.state_dict()) # for debug purposes\n",
    "        print('params: %d' % (n_unmask))\n",
    "    else:\n",
    "        print('-inf')\n",
    "        print('params: %d' % (net.get_nb_parameters()/2))\n",
    "    \n",
    "    test_cost = 0  # Note that these are per sample\n",
    "    test_err = 0\n",
    "    nb_samples = 0\n",
    "    for j, (x, y) in enumerate(valloader):\n",
    "        cost, err, probs = net.sample_eval(x, y, Nsamples_predict, logits=False) # , logits=True\n",
    "\n",
    "        test_cost += cost\n",
    "        test_err += err.cpu().numpy()\n",
    "        test_predictions[nb_samples:nb_samples+len(x), :] = probs.numpy()\n",
    "        nb_samples += len(x)\n",
    "\n",
    "    test_cost /= nb_samples\n",
    "    test_err /= nb_samples\n",
    "\n",
    "    print('Delete proportion: %f' % (p))\n",
    "    cprint('b', '    Jtest = %1.6f, err = %1.6f\\n' % (test_cost, test_err))\n",
    "    err_vec[idx] = test_err\n",
    "    \n",
    "    if p > 0:\n",
    "        net.model.load_state_dict(og_state_dict)\n",
    "    \n",
    "    \n",
    "plt.figure(dpi=100)\n",
    "plt.plot(delete_proportions, err_vec, 'o-')\n",
    "\n",
    "np.save(results_dir+'/snr_proportions', delete_proportions)\n",
    "np.save(results_dir+'/snr_prune_err', err_vec)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weight KLD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2395210,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'KLD density: Total parameters: 2395210, Nsamples: 20')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "name = 'Bayes_By_Backprop'\n",
    "Nsamples = 20\n",
    "# mkdir('snr_weights')\n",
    "KLD_vector = net.get_weight_KLD(Nsamples)\n",
    "np.save(results_dir+'/kld_vector_'+name+'.npy', KLD_vector)\n",
    "\n",
    "print(KLD_vector.shape)\n",
    "\n",
    "fig = plt.figure(dpi=100)\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "sns.distplot(KLD_vector, norm_hist=False, label=name, ax=ax)\n",
    "# ax.set_xlim((-2, 2))\n",
    "# ax.hist(weight_vector, bins=70, density=True);\n",
    "\n",
    "ax.set_ylabel('Density')\n",
    "ax.legend()\n",
    "plt.title('KLD density: Total parameters: %d, Nsamples: %d' % (len(KLD_vector), Nsamples))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2395210,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'KLD CDF: Total parameters: 23928000, Nsamples: 20')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "name = 'Bayes_By_Backprop'\n",
    "# Nsamples = 20\n",
    "# mkdir('snr_weights')\n",
    "# KLD_vector = net.get_weight_KLD(Nsamples)\n",
    "# np.save('weight_samples_'+name+'.npy', weight_vector)\n",
    "\n",
    "print(KLD_vector.shape)\n",
    "\n",
    "fig = plt.figure(dpi=100)\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "sns.distplot(KLD_vector, norm_hist=False, label=name, ax=ax, hist_kws=dict(cumulative=True))\n",
    "# ax.set_xlim((-2, 2))\n",
    "# ax.hist(weight_vector, bins=70, density=True);\n",
    "\n",
    "ax.set_ylabel('Cumulative Density')\n",
    "ax.legend()\n",
    "plt.title('KLD CDF: Total parameters: %d, Nsamples: %d' % (len(weight_vector), Nsamples))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### evaluate pruning performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0.1, 0.2, 0.4, 0.6, 0.8, 0.9, 0.99, 0.995]\n",
      "-inf\n",
      "params: 2395210\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:7: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.from_iter(generator)) or the python sum builtin instead.\n",
      "  import sys\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Delete proportion: 0.000000\n",
      "\u001b[34m    Jtest = 0.109638, err = 0.026400\n",
      "\u001b[0m\n",
      "-0.020493 nats\n",
      "params: 2156121\n",
      "Delete proportion: 0.100000\n",
      "\u001b[34m    Jtest = 0.106574, err = 0.025500\n",
      "\u001b[0m\n",
      "-0.003850 nats\n",
      "params: 1916669\n",
      "Delete proportion: 0.200000\n",
      "\u001b[34m    Jtest = 0.102950, err = 0.027100\n",
      "\u001b[0m\n",
      "0.018795 nats\n",
      "params: 1437948\n",
      "Delete proportion: 0.400000\n",
      "\u001b[34m    Jtest = 0.098232, err = 0.026900\n",
      "\u001b[0m\n",
      "0.064575 nats\n",
      "params: 958098\n",
      "Delete proportion: 0.600000\n",
      "\u001b[34m    Jtest = 0.096836, err = 0.027300\n",
      "\u001b[0m\n",
      "0.154137 nats\n",
      "params: 478676\n",
      "Delete proportion: 0.800000\n",
      "\u001b[34m    Jtest = 0.099576, err = 0.028700\n",
      "\u001b[0m\n",
      "0.227624 nats\n",
      "params: 239861\n",
      "Delete proportion: 0.900000\n",
      "\u001b[34m    Jtest = 0.110249, err = 0.032700\n",
      "\u001b[0m\n",
      "0.378996 nats\n",
      "params: 23902\n",
      "Delete proportion: 0.990000\n",
      "\u001b[34m    Jtest = 0.147214, err = 0.041000\n",
      "\u001b[0m\n",
      "0.417444 nats\n",
      "params: 11964\n",
      "Delete proportion: 0.995000\n",
      "\u001b[34m    Jtest = 0.156281, err = 0.043700\n",
      "\u001b[0m\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "delete_proportions = [0, 0.1, 0.2, 0.4, 0.6, 0.8, 0.9, 0.99, 0.995]# 0.9999, 0.99999, 0.999999, 0.9999999, 0.99999999] # \n",
    "# keep_proportions = 1 - delete_proportions\n",
    "\n",
    "\n",
    "batch_size = 256\n",
    "Nsamples_predict = 100\n",
    "# Nsamples_KLD = 20\n",
    "\n",
    "net.set_mode_train(False)\n",
    "\n",
    "if use_cuda:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=4)\n",
    "\n",
    "else:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=False,\n",
    "                                            num_workers=4)\n",
    "err_vec = np.zeros(len(delete_proportions))\n",
    "\n",
    "print(delete_proportions)\n",
    "\n",
    "for idx, p in enumerate(delete_proportions):\n",
    "    if p > 0:\n",
    "        min_kld = np.percentile(KLD_vector, p*100)\n",
    "        print('%f nats' % (min_kld))\n",
    "        og_state_dict, n_unmask = net.mask_model(Nsamples=20, thresh=min_kld)\n",
    "#         print(net.model.state_dict()) # for debug purposes\n",
    "        print('params: %d' % (n_unmask))\n",
    "    else:\n",
    "        print('-inf')\n",
    "        print('params: %d' % (net.get_nb_parameters()/2))\n",
    "    \n",
    "    test_cost = 0  # Note that these are per sample\n",
    "    test_err = 0\n",
    "    nb_samples = 0\n",
    "    for j, (x, y) in enumerate(valloader):\n",
    "        cost, err, probs = net.sample_eval(x, y, Nsamples_predict, logits=False) # , logits=True\n",
    "\n",
    "        test_cost += cost\n",
    "        test_err += err.cpu().numpy()\n",
    "        test_predictions[nb_samples:nb_samples+len(x), :] = probs.numpy()\n",
    "        nb_samples += len(x)\n",
    "\n",
    "    test_cost /= nb_samples\n",
    "    test_err /= nb_samples\n",
    "\n",
    "    print('Delete proportion: %f' % (p))\n",
    "    cprint('b', '    Jtest = %1.6f, err = %1.6f\\n' % (test_cost, test_err))\n",
    "    err_vec[idx] = test_err\n",
    "    \n",
    "    if p > 0:\n",
    "        net.model.load_state_dict(og_state_dict)\n",
    "    \n",
    "    \n",
    "plt.figure(dpi=100)\n",
    "plt.plot(delete_proportions, err_vec, 'o-')\n",
    "\n",
    "np.save(results_dir+'/kld_proportions', delete_proportions)\n",
    "np.save(results_dir+'/kld_prune_err', err_vec)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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   "execution_count": null,
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   "source": []
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   "execution_count": null,
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   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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